diff --git a/notebooks/data/city_temperature.csv b/notebooks/data/city_temperature.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9c82d76b23c67f310a77c3583c7663cd442af492
Binary files /dev/null and b/notebooks/data/city_temperature.csv differ
diff --git a/notebooks/jupyter_TP.ipynb b/notebooks/jupyter_TP.ipynb
index 013c199bfdbdf8916078bf8208115ff9a9a0110e..6ee8d269a4b2d86371a1cf3b306de1f502b90b7c 100644
--- a/notebooks/jupyter_TP.ipynb
+++ b/notebooks/jupyter_TP.ipynb
@@ -12,7 +12,7 @@
" <div>\n",
" Bertrand Néron, François Laurent, Etienne Kornobis\n",
" <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
+ " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistics HUB</a>\n",
" <br />\n",
" © Institut Pasteur, 2021\n",
" </div> \n",
diff --git a/notebooks/jupyter_cours.ipynb b/notebooks/jupyter_cours.ipynb
index da1628952f00d365af2b2bf97f5a2072fdd1847d..2affad5895623799bdfdff393198a6eac42408a4 100644
--- a/notebooks/jupyter_cours.ipynb
+++ b/notebooks/jupyter_cours.ipynb
@@ -12,7 +12,7 @@
" <div>\n",
" Bertrand Néron, François Laurent, Etienne Kornobis\n",
" <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
+ " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistics HUB</a>\n",
" <br />\n",
" © Institut Pasteur, 2021\n",
" </div> \n",
diff --git a/notebooks/pandas_TP.ipynb b/notebooks/pandas_TP.ipynb
index ea7191c7f3fc4c4891fbd23e8d6476ea722a4942..ab3b2c5306dbd4e30e4ba637be8358c5ceea682e 100644
--- a/notebooks/pandas_TP.ipynb
+++ b/notebooks/pandas_TP.ipynb
@@ -5,7 +5,7 @@
"id": "separated-samba",
"metadata": {},
"source": [
- "# <center>**TP**</center>\n",
+ "# <center><b>Hands-on</b></center>\n",
"\n",
"<img src=\"./images/pandas_logo.svg\">\n",
"<div style=\"text-align:center\">\n",
@@ -71,7 +71,7 @@
"id": "still-scheme",
"metadata": {},
"source": [
- "- Extract 3rd line from the ``blast_res`` dataframe. Which type of data structure is returned by this extraction ?"
+ "- Extract the 3rd line from the ``blast_res`` dataframe. Which type of data structure is returned by this extraction ?"
]
},
{
@@ -82,6 +82,22 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "id": "f80cc361-62ca-444e-a36a-810aebb4a7a8",
+ "metadata": {},
+ "source": [
+ "- Using iloc, extract the first 5 lines from the ``blast_res`` dataframe. Which type of data structure is returned by this extraction ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca60d822-08ae-4456-a424-3fec2a4bde9c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"id": "persistent-beijing",
@@ -98,6 +114,38 @@
"outputs": [],
"source": []
},
+ {
+ "cell_type": "markdown",
+ "id": "c0c9dbb9-936a-498d-a41a-83f8cd259f34",
+ "metadata": {},
+ "source": [
+ "- Select only the \"qseqid\", \"sseqid\", \"pident\", \"evalue\" and \"bitscore\" columns of the Dataframe: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5293eb0b-8d96-48e1-bf3f-8a37a7502d9c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f7ee8ca-ff29-4a36-941c-f000ae35edc4",
+ "metadata": {},
+ "source": [
+ "- Feel free to play around with DataFrame slicing (selecting different combinations of lines and columns) to get comfortable with the syntax. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dda0eb84-c727-40bf-a9b9-fb16d224fead",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"id": "generic-hearts",
@@ -215,7 +263,7 @@
"id": "destroyed-velvet",
"metadata": {},
"source": [
- "- Plot a barplot of the number of hits per species (species are considered the last code after the \"_\" in the sseqid column)"
+ "- Plot a barplot of the number of hits per species (species are considered the last code after the \"_\" in the sseqid column). You might to have to look at the documentation of [str.split method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.split.html) for this one."
]
},
{
@@ -242,7 +290,7 @@
"- Read the 'data/city_temperature.csv'\n",
"\n",
"- Force the City datatype to string by passing `dtype={'City': str}` as argument to the function to read the file.\n",
- "Don't worry to the warning, it is due to State wich contains Nan for non US contry, but we do not use these data."
+ "Don't worry for the warning, it is due to State wich contains Nan for non US contry, but we do not use these data."
]
},
{
@@ -459,7 +507,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -473,7 +521,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.2"
+ "version": "3.10.4"
}
},
"nbformat": 4,
diff --git a/notebooks/pandas_TP_solution.ipynb b/notebooks/pandas_TP_solution.ipynb
index 115f7964eb3d5cef77231b7f5b1e2fcad4e135bf..75f1767d4279ad5553bfcb3c9be07db01ee44fb3 100644
--- a/notebooks/pandas_TP_solution.ipynb
+++ b/notebooks/pandas_TP_solution.ipynb
@@ -5,7 +5,7 @@
"id": "integral-thermal",
"metadata": {},
"source": [
- "# <center>**TP**</center>\n",
+ "# <center><b>Hands-on</b></center>\n",
"\n",
"<img src=\"./images/pandas_logo.svg\">\n",
"<div style=\"text-align:center\">\n",
@@ -49,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"id": "loaded-transfer",
"metadata": {},
"outputs": [],
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"id": "streaming-regulation",
"metadata": {},
"outputs": [
@@ -70,7 +70,7 @@
"pandas.core.frame.DataFrame"
]
},
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -81,7 +81,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"id": "unsigned-coast",
"metadata": {},
"outputs": [
@@ -321,7 +321,7 @@
"[176 rows x 12 columns]"
]
},
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -345,7 +345,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"id": "simplified-progress",
"metadata": {},
"outputs": [
@@ -480,7 +480,7 @@
"4 1 316 1 316 0.0 515.0 "
]
},
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -491,7 +491,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"id": "narrow-smell",
"metadata": {},
"outputs": [
@@ -677,7 +677,7 @@
"175 95 155 15 62 1.000000e-06 50.1 "
]
},
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -688,7 +688,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"id": "identical-guest",
"metadata": {},
"outputs": [
@@ -856,7 +856,7 @@
"max 84.000000 362.000000 1.000000e-06 654.000000 "
]
},
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -867,7 +867,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 8,
"id": "alpine-cleveland",
"metadata": {},
"outputs": [
@@ -877,7 +877,7 @@
"(176, 12)"
]
},
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -896,7 +896,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 60,
"id": "complicated-football",
"metadata": {},
"outputs": [
@@ -918,7 +918,7 @@
"Name: 2, dtype: object"
]
},
- "execution_count": 11,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
@@ -929,7 +929,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 61,
"id": "administrative-biodiversity",
"metadata": {},
"outputs": [
@@ -939,7 +939,7 @@
"pandas.core.series.Series"
]
},
- "execution_count": 12,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@@ -948,6 +948,181 @@
"type(blast_res.iloc[2])"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "074339d0-1519-4059-bbe4-1802a50a731e",
+ "metadata": {},
+ "source": [
+ "- Using iloc, extract the first 5 lines from the ``blast_res`` dataframe. Which type of data structure is returned by this extraction ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "0367828c-9201-45ee-898c-36a85fd8817c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>qseqid</th>\n",
+ " <th>sseqid</th>\n",
+ " <th>pident</th>\n",
+ " <th>length</th>\n",
+ " <th>mismatch</th>\n",
+ " <th>gapopen</th>\n",
+ " <th>qstart</th>\n",
+ " <th>qend</th>\n",
+ " <th>sstart</th>\n",
+ " <th>send</th>\n",
+ " <th>evalue</th>\n",
+ " <th>bitscore</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|O60218|AK1BA_HUMAN</td>\n",
+ " <td>100.00</td>\n",
+ " <td>316</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>0.0</td>\n",
+ " <td>654.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|C9JRZ8|AK1BF_HUMAN</td>\n",
+ " <td>91.16</td>\n",
+ " <td>294</td>\n",
+ " <td>26</td>\n",
+ " <td>0</td>\n",
+ " <td>23</td>\n",
+ " <td>316</td>\n",
+ " <td>51</td>\n",
+ " <td>344</td>\n",
+ " <td>0.0</td>\n",
+ " <td>559.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|O08782|ALD2_CRIGR</td>\n",
+ " <td>83.23</td>\n",
+ " <td>316</td>\n",
+ " <td>53</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>0.0</td>\n",
+ " <td>537.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P45377|ALD2_MOUSE</td>\n",
+ " <td>82.28</td>\n",
+ " <td>316</td>\n",
+ " <td>56</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>0.0</td>\n",
+ " <td>527.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P21300|ALD1_MOUSE</td>\n",
+ " <td>79.75</td>\n",
+ " <td>316</td>\n",
+ " <td>64</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>1</td>\n",
+ " <td>316</td>\n",
+ " <td>0.0</td>\n",
+ " <td>515.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " qseqid sseqid pident length mismatch gapopen \\\n",
+ "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
+ "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
+ "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 316 53 0 \n",
+ "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 316 56 0 \n",
+ "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 316 64 0 \n",
+ "\n",
+ " qstart qend sstart send evalue bitscore \n",
+ "0 1 316 1 316 0.0 654.0 \n",
+ "1 23 316 51 344 0.0 559.0 \n",
+ "2 1 316 1 316 0.0 537.0 \n",
+ "3 1 316 1 316 0.0 527.0 \n",
+ "4 1 316 1 316 0.0 515.0 "
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "blast_res.iloc[:5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "b6180bb6-44c3-4ab8-9f74-fca7e7e3f733",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pandas.core.frame.DataFrame"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(blast_res.iloc[:5])"
+ ]
+ },
{
"cell_type": "markdown",
"id": "equipped-amendment",
@@ -958,7 +1133,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 11,
"id": "seasonal-europe",
"metadata": {},
"outputs": [
@@ -979,17 +1154,185 @@
"Name: sseqid, Length: 176, dtype: object"
]
},
- "execution_count": 20,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "blast_res.sseqid\n",
- "# OR\n",
- "blast_res['sseqid']\n",
- "# OR\n",
- "blast_res.loc[:,'sseqid']"
+ "blast_res.loc[:,'sseqid']\n",
+ "# Or\n",
+ "blast_res.sseqid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "55ff091c-365c-4174-b6d7-be39b0dbe11d",
+ "metadata": {},
+ "source": [
+ "- Select only the \"qseqid\", \"sseqid\", \"pident\", \"evalue\" and \"bitscore\" columns of the Dataframe: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "7cd85008-5b14-4a62-a195-7df323a0b8a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>qseqid</th>\n",
+ " <th>sseqid</th>\n",
+ " <th>pident</th>\n",
+ " <th>evalue</th>\n",
+ " <th>bitscore</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|O60218|AK1BA_HUMAN</td>\n",
+ " <td>100.00</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>654.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|C9JRZ8|AK1BF_HUMAN</td>\n",
+ " <td>91.16</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>559.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|O08782|ALD2_CRIGR</td>\n",
+ " <td>83.23</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>537.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P45377|ALD2_MOUSE</td>\n",
+ " <td>82.28</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>527.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P21300|ALD1_MOUSE</td>\n",
+ " <td>79.75</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>515.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>171</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P80874|GS69_BACSU</td>\n",
+ " <td>29.36</td>\n",
+ " <td>3.000000e-11</td>\n",
+ " <td>67.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>172</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|Q56Y42|PLR1_ARATH</td>\n",
+ " <td>23.00</td>\n",
+ " <td>6.000000e-09</td>\n",
+ " <td>60.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>173</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P25906|YDBC_ECOLI</td>\n",
+ " <td>23.75</td>\n",
+ " <td>6.000000e-09</td>\n",
+ " <td>59.7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>174</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|C6TBN2|AKR1_SOYBN</td>\n",
+ " <td>25.32</td>\n",
+ " <td>6.000000e-08</td>\n",
+ " <td>57.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>175</th>\n",
+ " <td>AK1BA_HUMAN</td>\n",
+ " <td>sp|P49261|CROB_LEPLU</td>\n",
+ " <td>45.90</td>\n",
+ " <td>1.000000e-06</td>\n",
+ " <td>50.1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>176 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " qseqid sseqid pident evalue bitscore\n",
+ "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 0.000000e+00 654.0\n",
+ "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 0.000000e+00 559.0\n",
+ "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 0.000000e+00 537.0\n",
+ "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 0.000000e+00 527.0\n",
+ "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 0.000000e+00 515.0\n",
+ ".. ... ... ... ... ...\n",
+ "171 AK1BA_HUMAN sp|P80874|GS69_BACSU 29.36 3.000000e-11 67.0\n",
+ "172 AK1BA_HUMAN sp|Q56Y42|PLR1_ARATH 23.00 6.000000e-09 60.1\n",
+ "173 AK1BA_HUMAN sp|P25906|YDBC_ECOLI 23.75 6.000000e-09 59.7\n",
+ "174 AK1BA_HUMAN sp|C6TBN2|AKR1_SOYBN 25.32 6.000000e-08 57.0\n",
+ "175 AK1BA_HUMAN sp|P49261|CROB_LEPLU 45.90 1.000000e-06 50.1\n",
+ "\n",
+ "[176 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "blast_res.loc[:, [\"qseqid\", \"sseqid\", \"pident\", \"evalue\", \"bitscore\"]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e3f2d8d-4a43-4719-8554-9b8b5b8b77f0",
+ "metadata": {},
+ "source": [
+ "- Feel free to play around with DataFrame slicing (selecting different combinations of lines and columns) to get comfortable with the syntax. "
]
},
{
@@ -1002,7 +1345,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 12,
"id": "varied-influence",
"metadata": {},
"outputs": [
@@ -1012,7 +1355,7 @@
"0.0"
]
},
- "execution_count": 21,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -1023,7 +1366,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 13,
"id": "little-recipient",
"metadata": {},
"outputs": [
@@ -1033,7 +1376,7 @@
"1e-06"
]
},
- "execution_count": 22,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1052,7 +1395,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 14,
"id": "polyphonic-retro",
"metadata": {},
"outputs": [
@@ -1062,7 +1405,7 @@
"205.0"
]
},
- "execution_count": 23,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -1073,7 +1416,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 15,
"id": "advisory-symphony",
"metadata": {},
"outputs": [
@@ -1083,7 +1426,7 @@
"231.9528409090909"
]
},
- "execution_count": 24,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -1102,7 +1445,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 16,
"id": "rough-globe",
"metadata": {},
"outputs": [
@@ -1254,7 +1597,7 @@
"5 1 316 1 316 2.000000e-177 501.0 "
]
},
- "execution_count": 25,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -1265,7 +1608,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 17,
"id": "novel-turkey",
"metadata": {},
"outputs": [
@@ -1417,7 +1760,7 @@
"5 1 316 1 316 2.000000e-177 501.0 "
]
},
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -1437,7 +1780,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 18,
"id": "arbitrary-style",
"metadata": {},
"outputs": [
@@ -1504,7 +1847,7 @@
"0 1 316 1 316 0.0 654.0 "
]
},
- "execution_count": 26,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -1526,7 +1869,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 20,
"id": "failing-crossing",
"metadata": {},
"outputs": [
@@ -1763,19 +2106,20 @@
"161 5 118 11 127 3.000000e-30 116.0 "
]
},
- "execution_count": 28,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This could be done with list comprehension creating a list of Booleans \n",
- "blast_res.loc[[\"HUMAN\" in x for x in blast_res.sseqid]]"
+ "mask =[\"HUMAN\" in x for x in blast_res.sseqid] \n",
+ "blast_res.loc[mask]"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 21,
"id": "trained-durham",
"metadata": {},
"outputs": [
@@ -2012,7 +2356,7 @@
"161 5 118 11 127 3.000000e-30 116.0 "
]
},
- "execution_count": 29,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -2024,7 +2368,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 24,
"id": "structural-hybrid",
"metadata": {},
"outputs": [
@@ -2142,7 +2486,7 @@
"5 1 316 1 316 2.000000e-177 501.0 "
]
},
- "execution_count": 38,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -2161,7 +2505,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 25,
"id": "liable-wheat",
"metadata": {},
"outputs": [
@@ -2171,13 +2515,13 @@
"<AxesSubplot:>"
]
},
- "execution_count": 32,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2202,7 +2546,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 26,
"id": "normal-glenn",
"metadata": {},
"outputs": [
@@ -2309,7 +2653,7 @@
"[176 rows x 2 columns]"
]
},
- "execution_count": 60,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -2362,16 +2706,6 @@
"# Extra exercise"
]
},
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "experienced-prediction",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd"
- ]
- },
{
"cell_type": "markdown",
"id": "purple-legend",
@@ -2389,7 +2723,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 31,
"id": "arctic-pickup",
"metadata": {},
"outputs": [
@@ -2397,8 +2731,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/home/bneron/Projects/MNE/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3165: DtypeWarning: Columns (2) have mixed types.Specify dtype option on import or set low_memory=False.\n",
- " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
+ "/tmp/ipykernel_3875/2154739564.py:1: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+ " world = pd.read_csv('data/city_temperature.csv' , sep=',', dtype={'City': str})\n"
]
}
],
@@ -2408,7 +2742,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 32,
"id": "conventional-section",
"metadata": {},
"outputs": [
@@ -2420,7 +2754,7 @@
" dtype='object')"
]
},
- "execution_count": 3,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -2434,12 +2768,12 @@
"id": "authentic-hearts",
"metadata": {},
"source": [
- "We will work only on Europe Region. so creat data named europe with only these data"
+ "We will work only on Europe Region. so create data named europe with only these data"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 33,
"id": "strong-skirt",
"metadata": {},
"outputs": [],
@@ -2452,12 +2786,12 @@
"id": "protected-desperate",
"metadata": {},
"source": [
- "wich country are in europe?"
+ "Which country are in europe?"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 34,
"id": "unable-establishment",
"metadata": {},
"outputs": [
@@ -2473,7 +2807,7 @@
" 'United Kingdom', 'Yugoslavia'], dtype=object)"
]
},
- "execution_count": 5,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -2492,7 +2826,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 35,
"id": "anticipated-illinois",
"metadata": {},
"outputs": [],
@@ -2510,7 +2844,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 36,
"id": "organized-tender",
"metadata": {},
"outputs": [],
@@ -2528,7 +2862,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 37,
"id": "sacred-secret",
"metadata": {},
"outputs": [
@@ -2550,7 +2884,7 @@
"Name: AvgTemperature, Length: 182, dtype: float64"
]
},
- "execution_count": 8,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -2570,7 +2904,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 38,
"id": "absent-envelope",
"metadata": {},
"outputs": [
@@ -2592,7 +2926,7 @@
"Name: AvgTemperature, Length: 182, dtype: float64"
]
},
- "execution_count": 9,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -2614,7 +2948,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 39,
"id": "geological-newman",
"metadata": {},
"outputs": [],
@@ -2635,7 +2969,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 40,
"id": "liquid-brighton",
"metadata": {},
"outputs": [
@@ -2766,7 +3100,7 @@
"[182 rows x 4 columns]"
]
},
- "execution_count": 11,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -2786,7 +3120,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 41,
"id": "agreed-diesel",
"metadata": {},
"outputs": [],
@@ -2806,13 +3140,13 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 42,
"id": "animated-alert",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2824,7 +3158,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2836,7 +3170,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2848,7 +3182,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2860,7 +3194,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2872,7 +3206,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2884,7 +3218,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2899,21 +3233,13 @@
"for city, df in clean_data.groupby('City'):\n",
" df.plot('Year', 'Tmp', label=city)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "random-mediterranean",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python [conda env:dev]",
"language": "python",
- "name": "python3"
+ "name": "conda-env-dev-py"
},
"language_info": {
"codemirror_mode": {
@@ -2925,7 +3251,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.10.4"
}
},
"nbformat": 4,
diff --git a/notebooks/pandas_cours.ipynb b/notebooks/pandas_cours.ipynb
index 2abd56aa3eb1be15357278e4e15a2bee41e212a9..f9078e24df5ba48b887eb97dca4a39b73936a779 100644
--- a/notebooks/pandas_cours.ipynb
+++ b/notebooks/pandas_cours.ipynb
@@ -5,14 +5,14 @@
"id": "horizontal-listening",
"metadata": {},
"source": [
- "# <center>**Cours**</center>\n",
+ "# <center><b>Course</b></center>\n",
"\n",
"<div style=\"text-align:center\">\n",
" <img src=\"images/pandas_logo.svg\" width=\"600px\">\n",
" <div>\n",
" Bertrand Néron, François Laurent, Etienne Kornobis\n",
" <br />\n",
- " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistiqucs HUB</a>\n",
+ " <a src=\" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/\">Bioinformatics and Biostatistics HUB</a>\n",
" <br />\n",
" © Institut Pasteur, 2021\n",
" </div> \n",
@@ -78,7 +78,7 @@
},
{
"cell_type": "code",
- "execution_count": 171,
+ "execution_count": 1,
"id": "executed-tsunami",
"metadata": {},
"outputs": [],
@@ -95,15 +95,15 @@
"# Series\n",
"\n",
"A Series is a one-dimensional array with axis labels. Labels do not need to be\n",
- "unique but must be hashable.\n",
+ "unique (but it's a better practice if they are) but must be hashable.\n",
"\n",
"To create a series, use the pandas `Series` object and specify a list or tuple\n",
- "of value to feed your serie with as the first argument:"
+ "of value to feed your series with as the first argument:"
]
},
{
"cell_type": "code",
- "execution_count": 172,
+ "execution_count": 2,
"id": "musical-civilization",
"metadata": {},
"outputs": [
@@ -113,19 +113,19 @@
"pandas.core.series.Series"
]
},
- "execution_count": 172,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_nolabel = pd.Series([1,2,3])\n",
- "type(serie_nolabel)"
+ "series_nolabel = pd.Series([1,2,3])\n",
+ "type(series_nolabel)"
]
},
{
"cell_type": "code",
- "execution_count": 173,
+ "execution_count": 3,
"id": "superb-relaxation",
"metadata": {},
"outputs": [
@@ -138,13 +138,13 @@
"dtype: int64"
]
},
- "execution_count": 173,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_nolabel"
+ "series_nolabel"
]
},
{
@@ -152,13 +152,12 @@
"id": "coordinated-issue",
"metadata": {},
"source": [
- "You can specify the labels of your Series by providing a list of labels as\n",
- "for the `index` argument:"
+ "You can specify the labels of your Series by providing a list of labels with the `index` argument:"
]
},
{
"cell_type": "code",
- "execution_count": 174,
+ "execution_count": 4,
"id": "received-flash",
"metadata": {},
"outputs": [
@@ -171,14 +170,14 @@
"dtype: int64"
]
},
- "execution_count": 174,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label = pd.Series([1,2,3], index=['A', 'B', 'C'])\n",
- "serie_label"
+ "series_label = pd.Series([1,2,3], index=['A', 'B', 'C'])\n",
+ "series_label"
]
},
{
@@ -191,7 +190,7 @@
},
{
"cell_type": "code",
- "execution_count": 175,
+ "execution_count": 5,
"id": "immune-physiology",
"metadata": {},
"outputs": [
@@ -201,18 +200,18 @@
"RangeIndex(start=0, stop=3, step=1)"
]
},
- "execution_count": 175,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_nolabel.index"
+ "series_nolabel.index"
]
},
{
"cell_type": "code",
- "execution_count": 176,
+ "execution_count": 6,
"id": "systematic-working",
"metadata": {},
"outputs": [
@@ -222,28 +221,130 @@
"Index(['A', 'B', 'C'], dtype='object')"
]
},
- "execution_count": 176,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.index"
+ "series_label.index"
]
},
{
"cell_type": "markdown",
- "id": "arctic-gibson",
+ "id": "18fe91f2-6032-456a-b808-2ab18fa994ba",
"metadata": {},
"source": [
- "## Indexing/Slicing\n",
- "\n",
- "In order to subset a serie based on an **integer index**, you can use the `iloc` attribute:"
+ "## Indexing/Slicing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f828aa6-64ef-4c5f-82ac-8e14ff768804",
+ "metadata": {},
+ "source": [
+ "The syntax used for slicing Series in pandas is similar to the syntax used to slice lists in python, see for example: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "73d895e3-0702-4025-b9b3-551912903922",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ex_list = [1,2,3]\n",
+ "ex_list[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0e2b7687-45c2-4674-b390-c23a22ae9858",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 2]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ex_list[0:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "32fef6c2-f717-4a39-972a-805c70a240a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[3, 2, 1]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ex_list[::-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "961217e5-5345-4015-bc3d-79a140bec151",
+ "metadata": {},
+ "source": [
+ "In order to subset a serie based on an **integer index**, you can use the `iloc` attribute with the same syntax as with lists slices:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "95bf2f46-a4ce-4a6a-a201-943b1cd3bf80",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1\n",
+ "1 2\n",
+ "2 3\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "series_nolabel"
]
},
{
"cell_type": "code",
- "execution_count": 177,
+ "execution_count": 11,
"id": "alternate-banks",
"metadata": {},
"outputs": [
@@ -253,18 +354,18 @@
"2"
]
},
- "execution_count": 177,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_nolabel.iloc[1]"
+ "series_nolabel.iloc[1]"
]
},
{
"cell_type": "code",
- "execution_count": 178,
+ "execution_count": 12,
"id": "standing-train",
"metadata": {},
"outputs": [
@@ -274,18 +375,18 @@
"2"
]
},
- "execution_count": 178,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.iloc[1]"
+ "series_label.iloc[1]"
]
},
{
"cell_type": "code",
- "execution_count": 179,
+ "execution_count": 13,
"id": "severe-correlation",
"metadata": {},
"outputs": [
@@ -297,18 +398,18 @@
"dtype: int64"
]
},
- "execution_count": 179,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.iloc[0:2]"
+ "series_label.iloc[0:2]"
]
},
{
"cell_type": "code",
- "execution_count": 180,
+ "execution_count": 14,
"id": "raising-grenada",
"metadata": {},
"outputs": [
@@ -321,13 +422,13 @@
"dtype: int64"
]
},
- "execution_count": 180,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.iloc[::-1]"
+ "series_label.iloc[::-1]"
]
},
{
@@ -340,7 +441,7 @@
},
{
"cell_type": "code",
- "execution_count": 181,
+ "execution_count": 15,
"id": "accompanied-pantyhose",
"metadata": {},
"outputs": [
@@ -350,13 +451,13 @@
"2"
]
},
- "execution_count": 181,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.loc[\"B\"]"
+ "series_label.loc[\"B\"]"
]
},
{
@@ -372,7 +473,7 @@
},
{
"cell_type": "code",
- "execution_count": 182,
+ "execution_count": 16,
"id": "comparative-guinea",
"metadata": {},
"outputs": [
@@ -382,13 +483,13 @@
"1"
]
},
- "execution_count": 182,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.A"
+ "series_label.A"
]
},
{
@@ -396,15 +497,15 @@
"id": "convenient-constitution",
"metadata": {},
"source": [
- "Serie objects benefit from many attributes and methods (see [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html)), lot's of them being common with pandas DataFrames. We will see some of the one listed below in action in the DataFrame section of this course.\n",
+ "Serie objects benefit from many attributes and methods (see [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html)), lot's of them being common with pandas DataFrames. We will see some of the ones listed below in action in the DataFrame section of this course.\n",
"\n",
"Here are some attributes of interest:\n",
"\n",
"|Attribute|Action|\n",
"|-|-|\n",
- "|index|Returns the index (0 axis labels) of the Serie|\n",
- "|name|Return the name of the Serie|\n",
- "|shape|Return the number of element in the Serie|\n",
+ "|index|Returns the index (0 axis labels) of the Series|\n",
+ "|name|Return the name of the Series|\n",
+ "|shape|Return the number of element in the Series|\n",
"\n",
"And some useful methods:\n",
"\n",
@@ -440,12 +541,12 @@
"\n",
"Comparison operators (ie `==`, `<`, `<=`, `>=`, `>`) can be used on Series as well as DataFrames for subsetting.\n",
"\n",
- "For example, we want to see which values are superior to one in our previous Serie:"
+ "For example, we want to see which values are superior to 1 in our previous Series:"
]
},
{
"cell_type": "code",
- "execution_count": 183,
+ "execution_count": 17,
"id": "million-richards",
"metadata": {},
"outputs": [
@@ -458,13 +559,13 @@
"dtype: bool"
]
},
- "execution_count": 183,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label > 1"
+ "series_label > 1"
]
},
{
@@ -472,12 +573,12 @@
"id": "unlike-monaco",
"metadata": {},
"source": [
- "Since `loc` can take list or Series of booleans as input, we can then apply this Boolean Serie as a mask for our Serie:"
+ "On top of labels, `loc` can actually take a list or Series of booleans as input. We can therefore apply this boolean Series `series_label > 1` as a mask:"
]
},
{
"cell_type": "code",
- "execution_count": 184,
+ "execution_count": 18,
"id": "ordered-rendering",
"metadata": {},
"outputs": [
@@ -489,13 +590,13 @@
"dtype: int64"
]
},
- "execution_count": 184,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label.loc[serie_label>1]"
+ "series_label.loc[series_label > 1]"
]
},
{
@@ -517,7 +618,7 @@
},
{
"cell_type": "code",
- "execution_count": 185,
+ "execution_count": 20,
"id": "least-cruise",
"metadata": {},
"outputs": [
@@ -530,13 +631,13 @@
"dtype: int64"
]
},
- "execution_count": 185,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label + serie_label"
+ "series_label + series_label"
]
},
{
@@ -549,7 +650,7 @@
},
{
"cell_type": "code",
- "execution_count": 186,
+ "execution_count": 21,
"id": "better-blame",
"metadata": {},
"outputs": [
@@ -562,13 +663,13 @@
"dtype: int64"
]
},
- "execution_count": 186,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "serie_label + serie_label.iloc[::-1]"
+ "series_label + series_label.iloc[::-1]"
]
},
{
@@ -593,7 +694,7 @@
},
{
"cell_type": "code",
- "execution_count": 187,
+ "execution_count": 22,
"id": "regulated-ready",
"metadata": {},
"outputs": [
@@ -646,7 +747,7 @@
"b 4 5 6"
]
},
- "execution_count": 187,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -661,7 +762,7 @@
},
{
"cell_type": "code",
- "execution_count": 188,
+ "execution_count": 23,
"id": "stable-discharge",
"metadata": {},
"outputs": [
@@ -671,7 +772,7 @@
"Index(['a', 'b'], dtype='object')"
]
},
- "execution_count": 188,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -682,7 +783,7 @@
},
{
"cell_type": "code",
- "execution_count": 189,
+ "execution_count": 24,
"id": "configured-coral",
"metadata": {},
"outputs": [
@@ -692,7 +793,7 @@
"Index(['A', 'B', 'C'], dtype='object')"
]
},
- "execution_count": 189,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -711,7 +812,7 @@
},
{
"cell_type": "code",
- "execution_count": 190,
+ "execution_count": 25,
"id": "facial-curve",
"metadata": {},
"outputs": [
@@ -778,7 +879,7 @@
"3 9 10 11"
]
},
- "execution_count": 190,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -798,7 +899,7 @@
},
{
"cell_type": "code",
- "execution_count": 191,
+ "execution_count": 26,
"id": "suspected-nirvana",
"metadata": {},
"outputs": [
@@ -854,7 +955,7 @@
"2 3 6"
]
},
- "execution_count": 191,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -882,7 +983,7 @@
},
{
"cell_type": "code",
- "execution_count": 192,
+ "execution_count": 27,
"id": "sonic-shock",
"metadata": {
"tags": []
@@ -897,14 +998,14 @@
"id": "about-cursor",
"metadata": {},
"source": [
- "We want to open *data/bar_data.tsv* file but the 2 first lines are comments and the separator between fields is *tab*\n",
+ "We want now to open *data/bar_data.tsv* file but the 2 first lines are comments and the separator between fields is *tab*\n",
"\n",
"See below the 5 first lines (using the `!` jupyter magic for bash subprocesses)"
]
},
{
"cell_type": "code",
- "execution_count": 193,
+ "execution_count": 28,
"id": "bridal-development",
"metadata": {},
"outputs": [
@@ -924,6 +1025,14 @@
"! head -5 data/bar_data.tsv"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "af8a2fc6-e7bc-41c3-92a7-650fb7b42b50",
+ "metadata": {},
+ "source": [
+ "You can import this file using the `sep` and `comment` arguments:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 194,
@@ -1021,12 +1130,12 @@
"id": "explicit-monitoring",
"metadata": {},
"source": [
- "If the data in the file are already indexed like in this one:"
+ "If the data in the file are already indexed like in the file _data_for_plt.csv_ :"
]
},
{
"cell_type": "code",
- "execution_count": 195,
+ "execution_count": 29,
"id": "allied-artist",
"metadata": {},
"outputs": [
@@ -1239,7 +1348,7 @@
},
{
"cell_type": "code",
- "execution_count": 198,
+ "execution_count": 30,
"id": "oriented-bleeding",
"metadata": {},
"outputs": [
@@ -1327,7 +1436,7 @@
"4 -1.37 361.37 448.864769 5.732690"
]
},
- "execution_count": 198,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1347,7 +1456,7 @@
},
{
"cell_type": "code",
- "execution_count": 199,
+ "execution_count": 31,
"id": "competent-negative",
"metadata": {},
"outputs": [
@@ -1359,7 +1468,7 @@
" [3, 6]])"
]
},
- "execution_count": 199,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -1370,7 +1479,7 @@
},
{
"cell_type": "code",
- "execution_count": 200,
+ "execution_count": 32,
"id": "fantastic-monday",
"metadata": {},
"outputs": [
@@ -1380,7 +1489,7 @@
"[[1, 4], [2, 5], [3, 6]]"
]
},
- "execution_count": 200,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -1401,7 +1510,7 @@
},
{
"cell_type": "code",
- "execution_count": 201,
+ "execution_count": 33,
"id": "simple-luxury",
"metadata": {},
"outputs": [],
@@ -1419,7 +1528,7 @@
},
{
"cell_type": "code",
- "execution_count": 202,
+ "execution_count": 37,
"id": "wound-asbestos",
"metadata": {},
"outputs": [
@@ -1427,15 +1536,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "The titanic dataset is 891 length\n",
- "The titanic dataset contains 891 rows x 12 columns\n"
+ "The titanic dataset is 891 in length.\n",
+ "The titanic dataset contains 891 rows x 12 columns.\n"
]
}
],
"source": [
- "print(f\"The titanic dataset is {len(titanic)} length\")\n",
+ "print(f\"The titanic dataset is {len(titanic)} in length.\")\n",
"rows, cols = titanic.shape\n",
- "print(f\"The titanic dataset contains {rows} rows x {cols} columns\")"
+ "print(f\"The titanic dataset contains {rows} rows x {cols} columns.\")"
]
},
{
@@ -1443,7 +1552,7 @@
"id": "equal-original",
"metadata": {},
"source": [
- "`head` to get the first lines of your dataframe:"
+ "`head` method is useful to get the first lines of your dataframe:"
]
},
{
@@ -1806,12 +1915,12 @@
"id": "tight-craps",
"metadata": {},
"source": [
- "`describe` to have basic descriptive statistics. The columns on which pandas cannot do statistics are omitted (Name, Sex, ...)"
+ "Use `describe` method to have basic descriptive statistics. The columns on which pandas cannot do statistics are omitted (Name, Sex, ...)"
]
},
{
"cell_type": "code",
- "execution_count": 206,
+ "execution_count": 39,
"id": "sunset-ballot",
"metadata": {},
"outputs": [
@@ -1952,7 +2061,7 @@
"max 6.000000 512.329200 "
]
},
- "execution_count": 206,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -1964,7 +2073,7 @@
},
{
"cell_type": "code",
- "execution_count": 207,
+ "execution_count": 43,
"id": "whole-township",
"metadata": {},
"outputs": [
@@ -1972,13 +2081,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "titanic have 12 cols\n",
- "desc have 7 cols\n"
+ "Titanic data have 12 cols\n",
+ "and describe output have 7 cols\n"
]
}
],
"source": [
- "print(f\"titanic have {len(titanic.columns)} cols\\ndesc have {len(desc.columns)} cols\")"
+ "print(f\"Titanic data have {len(titanic.columns)} cols\\nand describe output have {len(desc.columns)} cols\")"
]
},
{
@@ -1991,10 +2100,18 @@
},
{
"cell_type": "code",
- "execution_count": 208,
+ "execution_count": 44,
"id": "furnished-dealing",
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_6604/502188208.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n",
+ " titanic.median()\n"
+ ]
+ },
{
"data": {
"text/plain": [
@@ -2008,7 +2125,7 @@
"dtype: float64"
]
},
- "execution_count": 208,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -2017,6 +2134,42 @@
"titanic.median()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "30d40995-8649-4946-8db3-ce2f0f7ffd6e",
+ "metadata": {},
+ "source": [
+ "To prepare for future versions of pandas, better to select the columns you actually want the median to be calculated with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "2a6e3ac6-90fe-4d2a-94c4-0b2a0030a892",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "PassengerId 446.0000\n",
+ "Survived 0.0000\n",
+ "Pclass 3.0000\n",
+ "Age 28.0000\n",
+ "SibSp 0.0000\n",
+ "Parch 0.0000\n",
+ "Fare 14.4542\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic.median(numeric_only=True)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "protected-fleece",
@@ -2027,7 +2180,7 @@
},
{
"cell_type": "code",
- "execution_count": 209,
+ "execution_count": 55,
"id": "further-circular",
"metadata": {},
"outputs": [
@@ -2044,13 +2197,13 @@
"dtype: float64"
]
},
- "execution_count": 209,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.mean()"
+ "titanic.mean(numeric_only=True)"
]
},
{
@@ -2063,7 +2216,7 @@
},
{
"cell_type": "code",
- "execution_count": 210,
+ "execution_count": 57,
"id": "comprehensive-division",
"metadata": {},
"outputs": [
@@ -2075,7 +2228,7 @@
"Name: Sex, dtype: int64"
]
},
- "execution_count": 210,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -2094,7 +2247,7 @@
},
{
"cell_type": "code",
- "execution_count": 211,
+ "execution_count": 58,
"id": "universal-boutique",
"metadata": {},
"outputs": [
@@ -2104,7 +2257,7 @@
"80.0"
]
},
- "execution_count": 211,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -2115,7 +2268,7 @@
},
{
"cell_type": "code",
- "execution_count": 212,
+ "execution_count": 59,
"id": "several-principle",
"metadata": {},
"outputs": [
@@ -2125,7 +2278,7 @@
"0.42"
]
},
- "execution_count": 212,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -2152,7 +2305,7 @@
},
{
"cell_type": "code",
- "execution_count": 213,
+ "execution_count": 82,
"id": "received-editing",
"metadata": {},
"outputs": [
@@ -2219,7 +2372,7 @@
"3 9 10 11"
]
},
- "execution_count": 213,
+ "execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
@@ -2232,7 +2385,7 @@
},
{
"cell_type": "code",
- "execution_count": 214,
+ "execution_count": 83,
"id": "classified-pittsburgh",
"metadata": {},
"outputs": [
@@ -2242,7 +2395,7 @@
"Index(['A', 'B', 'Z'], dtype='object')"
]
},
- "execution_count": 214,
+ "execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
@@ -2256,7 +2409,7 @@
},
{
"cell_type": "code",
- "execution_count": 215,
+ "execution_count": 84,
"id": "exceptional-roberts",
"metadata": {},
"outputs": [
@@ -2323,7 +2476,7 @@
"3 9 10 11"
]
},
- "execution_count": 215,
+ "execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
@@ -2333,9 +2486,17 @@
"df"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "06de9c56-e620-438b-a735-d4478dd2cb16",
+ "metadata": {},
+ "source": [
+ "Using the `rename` method and specifying a dictionnary for the changes:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 216,
+ "execution_count": 85,
"id": "surprised-burns",
"metadata": {},
"outputs": [
@@ -2402,7 +2563,7 @@
"3 9 10 11"
]
},
- "execution_count": 216,
+ "execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
@@ -2416,12 +2577,12 @@
"id": "novel-sheet",
"metadata": {},
"source": [
- "### Rename index"
+ "### Renaming index"
]
},
{
"cell_type": "code",
- "execution_count": 217,
+ "execution_count": 86,
"id": "breathing-yeast",
"metadata": {},
"outputs": [
@@ -2488,7 +2649,7 @@
"e 9 10 11"
]
},
- "execution_count": 217,
+ "execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
@@ -2500,7 +2661,7 @@
},
{
"cell_type": "code",
- "execution_count": 218,
+ "execution_count": 87,
"id": "central-columbus",
"metadata": {},
"outputs": [
@@ -2567,7 +2728,7 @@
"d 9 10 11"
]
},
- "execution_count": 218,
+ "execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
@@ -2581,12 +2742,12 @@
"id": "august-store",
"metadata": {},
"source": [
- "### Add column"
+ "### Adding columns"
]
},
{
"cell_type": "code",
- "execution_count": 219,
+ "execution_count": 88,
"id": "outer-access",
"metadata": {},
"outputs": [
@@ -2658,7 +2819,7 @@
"e 9 10 11 12"
]
},
- "execution_count": 219,
+ "execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
@@ -2668,9 +2829,17 @@
"df"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "26040993-b075-423f-9454-6e58230f00df",
+ "metadata": {},
+ "source": [
+ "You can also add columns by concatenating two dataframes with the `concat` method (axis argument is use to perform the concatenation along lines or along columns):"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 220,
+ "execution_count": 89,
"id": "respective-twins",
"metadata": {},
"outputs": [
@@ -2762,7 +2931,7 @@
"e 9 10 11 12 9 10 11 12"
]
},
- "execution_count": 220,
+ "execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
@@ -2783,7 +2952,7 @@
},
{
"cell_type": "code",
- "execution_count": 221,
+ "execution_count": 90,
"id": "blank-ceiling",
"metadata": {},
"outputs": [
@@ -2857,7 +3026,7 @@
"12 9 10 11"
]
},
- "execution_count": 221,
+ "execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
@@ -2866,9 +3035,17 @@
"df.set_index(\"id\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "9a2674fa-55f1-449c-ba50-639c8d57479a",
+ "metadata": {},
+ "source": [
+ "We can see here that the original dataframe is not modified with this method:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 222,
+ "execution_count": 91,
"id": "checked-prototype",
"metadata": {},
"outputs": [
@@ -2940,7 +3117,7 @@
"e 9 10 11 12"
]
},
- "execution_count": 222,
+ "execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
@@ -2959,7 +3136,7 @@
},
{
"cell_type": "code",
- "execution_count": 223,
+ "execution_count": 92,
"id": "alpine-coast",
"metadata": {},
"outputs": [],
@@ -2969,7 +3146,7 @@
},
{
"cell_type": "code",
- "execution_count": 224,
+ "execution_count": 93,
"id": "gothic-freight",
"metadata": {},
"outputs": [
@@ -3043,7 +3220,7 @@
"12 9 10 11"
]
},
- "execution_count": 224,
+ "execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
@@ -3058,12 +3235,12 @@
"metadata": {},
"source": [
"### Reset index\n",
- "The opposite operation in to turn the index into a normal column and regenerate a basic integer index"
+ "The opposite operation is to turn the index into a normal column and regenerate a basic integer index"
]
},
{
"cell_type": "code",
- "execution_count": 225,
+ "execution_count": 94,
"id": "signal-disabled",
"metadata": {},
"outputs": [
@@ -3135,7 +3312,7 @@
"3 12 9 10 11"
]
},
- "execution_count": 225,
+ "execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
@@ -3157,7 +3334,7 @@
},
{
"cell_type": "code",
- "execution_count": 226,
+ "execution_count": 96,
"id": "western-roots",
"metadata": {},
"outputs": [],
@@ -3175,7 +3352,7 @@
},
{
"cell_type": "code",
- "execution_count": 227,
+ "execution_count": 97,
"id": "sophisticated-speaking",
"metadata": {},
"outputs": [
@@ -3256,7 +3433,7 @@
"1 42 43 44"
]
},
- "execution_count": 227,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
@@ -3270,12 +3447,12 @@
"id": "funny-choice",
"metadata": {},
"source": [
- "You can choose also to `ignore_index`, similar to reseting and dropping the indices (but note that the index values on the other axes are still respected in the join):"
+ "You can choose also to `ignore_index`, similar to reseting and dropping the indices (but note that the index values on the other axes (i.e. the columns) are still respected in the concatenation):"
]
},
{
"cell_type": "code",
- "execution_count": 228,
+ "execution_count": 99,
"id": "associate-lodge",
"metadata": {},
"outputs": [
@@ -3370,7 +3547,7 @@
"7 9 10 11"
]
},
- "execution_count": 228,
+ "execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
@@ -3381,17 +3558,25 @@
},
{
"cell_type": "markdown",
- "id": "integrated-suggestion",
+ "id": "employed-extension",
"metadata": {},
"source": [
- "## Filtering tables\n",
+ "### Indexing/Slicing\n",
"\n",
- "> https://pandas.pydata.org/docs/user_guide/indexing.html#indexing"
+ "> https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html\n",
+ "\n",
+ "As for pandas Series, you can use `loc` (with labels or booleans) and `iloc` (with integers) for indexing/slicing.\n",
+ "\n",
+ "The first argument between square brackets represents rows and the second columns, i.e:\n",
+ "\n",
+ "**[row indices, column indices]**\n",
+ "\n",
+ "Both methods use the same syntax as numpy indexing/slicing."
]
},
{
"cell_type": "code",
- "execution_count": 229,
+ "execution_count": 116,
"id": "dying-hepatitis",
"metadata": {},
"outputs": [
@@ -3533,7 +3718,7 @@
"4 0 373450 8.0500 NaN S "
]
},
- "execution_count": 229,
+ "execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
@@ -3542,44 +3727,10 @@
"titanic.head()"
]
},
- {
- "cell_type": "markdown",
- "id": "growing-norfolk",
- "metadata": {},
- "source": [
- "### Selecting columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 230,
- "id": "homeless-debut",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 male\n",
- "1 female\n",
- "2 female\n",
- "3 female\n",
- "4 male\n",
- "Name: Sex, dtype: object"
- ]
- },
- "execution_count": 230,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "titanic['Sex'].head()"
- ]
- },
{
"cell_type": "code",
- "execution_count": 231,
- "id": "operating-rehabilitation",
+ "execution_count": 117,
+ "id": "authentic-winter",
"metadata": {},
"outputs": [
{
@@ -3605,80 +3756,42 @@
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
- " <th>Pclass</th>\n",
- " <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>0</th>\n",
- " <td>male</td>\n",
- " <td>22.0</td>\n",
- " <td>3</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
" <th>1</th>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>3</td>\n",
- " <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " Sex Age Pclass Survived\n",
- "0 male 22.0 3 0\n",
- "1 female 38.0 1 1\n",
- "2 female 26.0 3 1\n",
- "3 female 35.0 1 1\n",
- "4 male 35.0 3 0"
+ " Sex Age\n",
+ "1 female 38.0\n",
+ "2 female 26.0"
]
},
- "execution_count": 231,
+ "execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic[['Sex', 'Age', 'Pclass', 'Survived']].head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "innocent-hopkins",
- "metadata": {},
- "source": [
- "### Selecting on a condition"
+ "titanic.loc[[1,2], ['Sex', 'Age']]"
]
},
{
"cell_type": "code",
- "execution_count": 232,
- "id": "realistic-liberal",
+ "execution_count": 118,
+ "id": "partial-trading",
"metadata": {},
"outputs": [
{
@@ -3702,151 +3815,71 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>PassengerId</th>\n",
- " <th>Survived</th>\n",
- " <th>Pclass</th>\n",
- " <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
- " <th>Fare</th>\n",
- " <th>Cabin</th>\n",
- " <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
- " <td>71.2833</td>\n",
- " <td>C85</td>\n",
- " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
- " <td>7.9250</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>5</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
- " <td>8.0500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>7</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>McCarthy, Mr. Timothy J</td>\n",
- " <td>male</td>\n",
- " <td>54.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>17463</td>\n",
- " <td>51.8625</td>\n",
- " <td>E46</td>\n",
- " <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass \\\n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "4 5 0 3 \n",
- "6 7 0 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "4 Allen, Mr. William Henry male 35.0 0 \n",
- "6 McCarthy, Mr. Timothy J male 54.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "4 0 373450 8.0500 NaN S \n",
- "6 0 17463 51.8625 E46 S "
+ " Sex Age SibSp Parch Ticket\n",
+ "1 female 38.0 1 0 PC 17599\n",
+ "2 female 26.0 0 0 STON/O2. 3101282\n",
+ "3 female 35.0 1 0 113803\n",
+ "4 male 35.0 0 0 373450"
]
},
- "execution_count": 232,
+ "execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic[titanic['Age'] > 25].head()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "employed-extension",
- "metadata": {},
- "source": [
- "### Indexing/Slicing\n",
- "\n",
- "> https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html\n",
- "\n",
- "As for pandas Series, you can use `loc` (with labels or booleans) and `iloc` (with integers) for indexing/slicing.\n",
- "The first argument represent rows and the second columns.\n",
- "\n",
- "Both methods use the same syntax as numpy indexing/slicing"
+ "titanic.loc[1:4, 'Sex':'Ticket'] # Ticket column is included"
]
},
{
"cell_type": "code",
- "execution_count": 233,
- "id": "authentic-winter",
+ "execution_count": 119,
+ "id": "electrical-force",
"metadata": {},
"outputs": [
{
@@ -3876,14 +3909,14 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
+ " <th>0</th>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
+ " <th>1</th>\n",
" <td>female</td>\n",
- " <td>26.0</td>\n",
+ " <td>38.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -3891,23 +3924,23 @@
],
"text/plain": [
" Sex Age\n",
- "1 female 38.0\n",
- "2 female 26.0"
+ "0 male 22.0\n",
+ "1 female 38.0"
]
},
- "execution_count": 233,
+ "execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.loc[[1,2], ['Sex', 'Age']]"
+ "titanic.iloc[[0,1], [4, 5]]"
]
},
{
"cell_type": "code",
- "execution_count": 234,
- "id": "partial-trading",
+ "execution_count": 120,
+ "id": "after-giving",
"metadata": {},
"outputs": [
{
@@ -3940,6 +3973,14 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
+ " <th>0</th>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <th>1</th>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
@@ -3955,47 +3996,116 @@
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>female</td>\n",
- " <td>35.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>male</td>\n",
- " <td>35.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>373450</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex Age SibSp Parch Ticket\n",
+ "0 male 22.0 1 0 A/5 21171\n",
"1 female 38.0 1 0 PC 17599\n",
- "2 female 26.0 0 0 STON/O2. 3101282\n",
- "3 female 35.0 1 0 113803\n",
- "4 male 35.0 0 0 373450"
+ "2 female 26.0 0 0 STON/O2. 3101282"
]
},
- "execution_count": 234,
+ "execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.loc[1:4, 'Sex':'Ticket'] # Ticket column is included"
+ "titanic.iloc[0:3, 4:9] # the 9th column is exclude"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ded492b4-43d8-4835-b610-c43f33c91e47",
+ "metadata": {},
+ "source": [
+ "### Selecting columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03690fe3-a7f2-494a-8fe8-6bbb709b47a6",
+ "metadata": {},
+ "source": [
+ "A single column selection returns a Series:"
]
},
{
"cell_type": "code",
- "execution_count": 235,
- "id": "electrical-force",
+ "execution_count": 124,
+ "id": "homeless-debut",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 male\n",
+ "1 female\n",
+ "2 female\n",
+ "3 female\n",
+ "4 male\n",
+ "Name: Sex, dtype: object"
+ ]
+ },
+ "execution_count": 124,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic.loc[:,'Sex'].head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83f4873a-fa16-46ea-a2b1-b93e7d67ea49",
+ "metadata": {},
+ "source": [
+ "This syntax can be used as well for the same output:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 180,
+ "id": "c0edb00d-037e-45b1-9003-d16bef62258e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 male\n",
+ "1 female\n",
+ "2 female\n",
+ "3 female\n",
+ "4 male\n",
+ "Name: Sex, dtype: object"
+ ]
+ },
+ "execution_count": 180,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic.Sex.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29633cef-ad3f-496b-a0f7-8880fe9e7a14",
+ "metadata": {},
+ "source": [
+ "Multiple columns selection returns a DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "id": "operating-rehabilitation",
"metadata": {},
"outputs": [
{
@@ -4021,6 +4131,8 @@
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
+ " <th>Pclass</th>\n",
+ " <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -4028,35 +4140,79 @@
" <th>0</th>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " Sex Age\n",
- "0 male 22.0\n",
- "1 female 38.0"
+ " Sex Age Pclass Survived\n",
+ "0 male 22.0 3 0\n",
+ "1 female 38.0 1 1\n",
+ "2 female 26.0 3 1\n",
+ "3 female 35.0 1 1\n",
+ "4 male 35.0 3 0"
]
},
- "execution_count": 235,
+ "execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.iloc[[0,1], [4, 5]]"
+ "titanic.loc[:,['Sex', 'Age', 'Pclass', 'Survived']].head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c330ddf4-cc6d-4c9b-a176-a8d7f456da5a",
+ "metadata": {},
+ "source": [
+ "### Selecting on a condition"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14ba6f55-0231-4630-b6b4-8b47e7d270d3",
+ "metadata": {},
+ "source": [
+ "As in Series, `loc` accepts boolean arguments for selection:"
]
},
{
"cell_type": "code",
- "execution_count": 236,
- "id": "after-giving",
+ "execution_count": 131,
+ "id": "realistic-liberal",
"metadata": {},
"outputs": [
{
@@ -4080,61 +4236,144 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
+ " <th>PassengerId</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Pclass</th>\n",
+ " <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>0</th>\n",
+ " <th>33</th>\n",
+ " <td>34</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>Wheadon, Mr. Edward H</td>\n",
" <td>male</td>\n",
- " <td>22.0</td>\n",
+ " <td>66.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>C.A. 24579</td>\n",
+ " <td>10.5000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td>55</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
+ " <td>Ostby, Mr. Engelhart Cornelius</td>\n",
+ " <td>male</td>\n",
+ " <td>65.0</td>\n",
" <td>0</td>\n",
- " <td>A/5 21171</td>\n",
+ " <td>1</td>\n",
+ " <td>113509</td>\n",
+ " <td>61.9792</td>\n",
+ " <td>B30</td>\n",
+ " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1</th>\n",
- " <td>female</td>\n",
- " <td>38.0</td>\n",
+ " <th>96</th>\n",
+ " <td>97</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
+ " <td>Goldschmidt, Mr. George B</td>\n",
+ " <td>male</td>\n",
+ " <td>71.0</td>\n",
" <td>0</td>\n",
- " <td>PC 17599</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17754</td>\n",
+ " <td>34.6542</td>\n",
+ " <td>A5</td>\n",
+ " <td>C</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
- " <td>female</td>\n",
- " <td>26.0</td>\n",
+ " <th>116</th>\n",
+ " <td>117</td>\n",
" <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Connors, Mr. Patrick</td>\n",
+ " <td>male</td>\n",
+ " <td>70.5</td>\n",
" <td>0</td>\n",
- " <td>STON/O2. 3101282</td>\n",
+ " <td>0</td>\n",
+ " <td>370369</td>\n",
+ " <td>7.7500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Q</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>170</th>\n",
+ " <td>171</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>Van der hoef, Mr. Wyckoff</td>\n",
+ " <td>male</td>\n",
+ " <td>61.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>111240</td>\n",
+ " <td>33.5000</td>\n",
+ " <td>B19</td>\n",
+ " <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " Sex Age SibSp Parch Ticket\n",
- "0 male 22.0 1 0 A/5 21171\n",
- "1 female 38.0 1 0 PC 17599\n",
- "2 female 26.0 0 0 STON/O2. 3101282"
+ " PassengerId Survived Pclass Name Sex \\\n",
+ "33 34 0 2 Wheadon, Mr. Edward H male \n",
+ "54 55 0 1 Ostby, Mr. Engelhart Cornelius male \n",
+ "96 97 0 1 Goldschmidt, Mr. George B male \n",
+ "116 117 0 3 Connors, Mr. Patrick male \n",
+ "170 171 0 1 Van der hoef, Mr. Wyckoff male \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "33 66.0 0 0 C.A. 24579 10.5000 NaN S \n",
+ "54 65.0 0 1 113509 61.9792 B30 C \n",
+ "96 71.0 0 0 PC 17754 34.6542 A5 C \n",
+ "116 70.5 0 0 370369 7.7500 NaN Q \n",
+ "170 61.0 0 0 111240 33.5000 B19 S "
]
},
- "execution_count": 236,
+ "execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.iloc[0:3, 4:9] # the 9th column is exclude"
+ "titanic.loc[titanic['Age'] > 60].head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47113895-ff25-4644-8ae1-9635aee6e404",
+ "metadata": {},
+ "source": [
+ "We will see also the `query` method select on conditions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a31f8f3-6ecd-4afe-ba29-5606322e1504",
+ "metadata": {},
+ "source": [
+ "When working on columns containing strings, useful methods like `str.contains` are available for subsetting:"
]
},
{
"cell_type": "code",
- "execution_count": 237,
+ "execution_count": 136,
"id": "charming-debate",
"metadata": {},
"outputs": [
@@ -4196,14 +4435,14 @@
"871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 47.0"
]
},
- "execution_count": 237,
+ "execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "mask = titanic['Name'].str.contains('^Bec')\n",
- "titanic[mask][['Name', 'Age']]"
+ "mask = titanic.loc[:,'Name'].str.contains('^Bec')\n",
+ "titanic.loc[mask, ['Name', 'Age']]"
]
},
{
@@ -4218,7 +4457,7 @@
},
{
"cell_type": "code",
- "execution_count": 238,
+ "execution_count": 137,
"id": "extensive-sense",
"metadata": {},
"outputs": [
@@ -4259,118 +4498,118 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>736</th>\n",
- " <td>737</td>\n",
+ " <th>320</th>\n",
+ " <td>321</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Ford, Mrs. Edward (Margaret Ann Watson)</td>\n",
- " <td>female</td>\n",
- " <td>48.0</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>W./C. 6608</td>\n",
- " <td>34.3750</td>\n",
+ " <td>Dennis, Mr. Samuel</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21172</td>\n",
+ " <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>668</th>\n",
- " <td>669</td>\n",
+ " <th>59</th>\n",
+ " <td>60</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Cook, Mr. Jacob</td>\n",
+ " <td>Goodwin, Master. William Frederick</td>\n",
" <td>male</td>\n",
- " <td>43.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>A/5 3536</td>\n",
- " <td>8.0500</td>\n",
+ " <td>11.0</td>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>CA 2144</td>\n",
+ " <td>46.9000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>36</th>\n",
- " <td>37</td>\n",
- " <td>1</td>\n",
+ " <th>176</th>\n",
+ " <td>177</td>\n",
+ " <td>0</td>\n",
" <td>3</td>\n",
- " <td>Mamee, Mr. Hanna</td>\n",
+ " <td>Lefebre, Master. Henry Forbes</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2677</td>\n",
- " <td>7.2292</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>4133</td>\n",
+ " <td>25.4667</td>\n",
" <td>NaN</td>\n",
- " <td>C</td>\n",
+ " <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>145</th>\n",
- " <td>146</td>\n",
+ " <th>117</th>\n",
+ " <td>118</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
- " <td>Nicholls, Mr. Joseph Charles</td>\n",
+ " <td>Turpin, Mr. William John Robert</td>\n",
" <td>male</td>\n",
- " <td>19.0</td>\n",
+ " <td>29.0</td>\n",
" <td>1</td>\n",
- " <td>1</td>\n",
- " <td>C.A. 33112</td>\n",
- " <td>36.7500</td>\n",
+ " <td>0</td>\n",
+ " <td>11668</td>\n",
+ " <td>21.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>386</th>\n",
- " <td>387</td>\n",
+ " <th>714</th>\n",
+ " <td>715</td>\n",
" <td>0</td>\n",
- " <td>3</td>\n",
- " <td>Goodwin, Master. Sidney Leonard</td>\n",
- " <td>male</td>\n",
- " <td>1.0</td>\n",
- " <td>5</td>\n",
" <td>2</td>\n",
- " <td>CA 2144</td>\n",
- " <td>46.9000</td>\n",
+ " <td>Greenberg, Mr. Samuel</td>\n",
+ " <td>male</td>\n",
+ " <td>52.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>250647</td>\n",
+ " <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>151</th>\n",
- " <td>152</td>\n",
+ " <th>61</th>\n",
+ " <td>62</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
- " <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
+ " <td>Icard, Miss. Amelie</td>\n",
" <td>female</td>\n",
- " <td>22.0</td>\n",
- " <td>1</td>\n",
+ " <td>38.0</td>\n",
" <td>0</td>\n",
- " <td>113776</td>\n",
- " <td>66.6000</td>\n",
- " <td>C2</td>\n",
- " <td>S</td>\n",
+ " <td>0</td>\n",
+ " <td>113572</td>\n",
+ " <td>80.0000</td>\n",
+ " <td>B28</td>\n",
+ " <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass Name \\\n",
- "736 737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) \n",
- "668 669 0 3 Cook, Mr. Jacob \n",
- "36 37 1 3 Mamee, Mr. Hanna \n",
- "145 146 0 2 Nicholls, Mr. Joseph Charles \n",
- "386 387 0 3 Goodwin, Master. Sidney Leonard \n",
- "151 152 1 1 Pears, Mrs. Thomas (Edith Wearne) \n",
- "\n",
- " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
- "736 female 48.0 1 3 W./C. 6608 34.3750 NaN S \n",
- "668 male 43.0 0 0 A/5 3536 8.0500 NaN S \n",
- "36 male NaN 0 0 2677 7.2292 NaN C \n",
- "145 male 19.0 1 1 C.A. 33112 36.7500 NaN S \n",
- "386 male 1.0 5 2 CA 2144 46.9000 NaN S \n",
- "151 female 22.0 1 0 113776 66.6000 C2 S "
+ " PassengerId Survived Pclass Name \\\n",
+ "320 321 0 3 Dennis, Mr. Samuel \n",
+ "59 60 0 3 Goodwin, Master. William Frederick \n",
+ "176 177 0 3 Lefebre, Master. Henry Forbes \n",
+ "117 118 0 2 Turpin, Mr. William John Robert \n",
+ "714 715 0 2 Greenberg, Mr. Samuel \n",
+ "61 62 1 1 Icard, Miss. Amelie \n",
+ "\n",
+ " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "320 male 22.0 0 0 A/5 21172 7.2500 NaN S \n",
+ "59 male 11.0 5 2 CA 2144 46.9000 NaN S \n",
+ "176 male NaN 3 1 4133 25.4667 NaN S \n",
+ "117 male 29.0 1 0 11668 21.0000 NaN S \n",
+ "714 male 52.0 0 0 250647 13.0000 NaN S \n",
+ "61 female 38.0 0 0 113572 80.0000 B28 NaN "
]
},
- "execution_count": 238,
+ "execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
@@ -4391,7 +4630,7 @@
},
{
"cell_type": "code",
- "execution_count": 239,
+ "execution_count": 138,
"id": "enormous-dublin",
"metadata": {},
"outputs": [
@@ -4492,7 +4731,7 @@
"456 male 65.0 0 0 13509 26.550 E38 S "
]
},
- "execution_count": 239,
+ "execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
@@ -4516,7 +4755,7 @@
},
{
"cell_type": "code",
- "execution_count": 240,
+ "execution_count": 139,
"id": "piano-chance",
"metadata": {},
"outputs": [
@@ -4584,7 +4823,7 @@
"4 4 5"
]
},
- "execution_count": 240,
+ "execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
@@ -4596,7 +4835,7 @@
},
{
"cell_type": "code",
- "execution_count": 241,
+ "execution_count": 140,
"id": "minute-printer",
"metadata": {},
"outputs": [
@@ -4664,7 +4903,7 @@
"4 0 0"
]
},
- "execution_count": 241,
+ "execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
@@ -4675,7 +4914,7 @@
},
{
"cell_type": "code",
- "execution_count": 242,
+ "execution_count": 141,
"id": "polar-offering",
"metadata": {},
"outputs": [
@@ -4743,7 +4982,7 @@
"4 4 5"
]
},
- "execution_count": 242,
+ "execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
@@ -4766,7 +5005,7 @@
},
{
"cell_type": "code",
- "execution_count": 243,
+ "execution_count": 142,
"id": "designing-capacity",
"metadata": {},
"outputs": [
@@ -4834,7 +5073,7 @@
"4 4 5"
]
},
- "execution_count": 243,
+ "execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
@@ -4845,7 +5084,7 @@
},
{
"cell_type": "code",
- "execution_count": 244,
+ "execution_count": 143,
"id": "breeding-radio",
"metadata": {},
"outputs": [
@@ -4913,7 +5152,7 @@
"4 4 5"
]
},
- "execution_count": 244,
+ "execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
@@ -4936,7 +5175,7 @@
},
{
"cell_type": "code",
- "execution_count": 245,
+ "execution_count": 145,
"id": "systematic-hawaii",
"metadata": {},
"outputs": [
@@ -5015,187 +5254,76 @@
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
- " <td>0</td>\n",
- " <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>9</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
- " <td>female</td>\n",
- " <td>27.0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>347742</td>\n",
- " <td>11.1333</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>10</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
- " <td>female</td>\n",
- " <td>14.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>237736</td>\n",
- " <td>30.0708</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>875</th>\n",
- " <td>876</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
- " <td>female</td>\n",
- " <td>15.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2667</td>\n",
- " <td>7.2250</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>879</th>\n",
- " <td>880</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
- " <td>female</td>\n",
- " <td>56.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>11767</td>\n",
- " <td>83.1583</td>\n",
- " <td>C50</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>880</th>\n",
- " <td>881</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
- " <td>female</td>\n",
- " <td>25.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>230433</td>\n",
- " <td>26.0000</td>\n",
- " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>887</th>\n",
- " <td>888</td>\n",
- " <td>1</td>\n",
+ " <th>8</th>\n",
+ " <td>9</td>\n",
" <td>1</td>\n",
- " <td>Graham, Miss. Margaret Edith</td>\n",
+ " <td>3</td>\n",
+ " <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
- " <td>19.0</td>\n",
- " <td>0</td>\n",
+ " <td>27.0</td>\n",
" <td>0</td>\n",
- " <td>112053</td>\n",
- " <td>30.0000</td>\n",
- " <td>B42</td>\n",
+ " <td>2</td>\n",
+ " <td>347742</td>\n",
+ " <td>11.1333</td>\n",
+ " <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>889</th>\n",
- " <td>890</td>\n",
+ " <th>9</th>\n",
+ " <td>10</td>\n",
" <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
+ " <td>female</td>\n",
+ " <td>14.0</td>\n",
" <td>1</td>\n",
- " <td>Behr, Mr. Karl Howell</td>\n",
- " <td>male</td>\n",
- " <td>26.0</td>\n",
- " <td>0</td>\n",
" <td>0</td>\n",
- " <td>111369</td>\n",
- " <td>30.0000</td>\n",
- " <td>C148</td>\n",
+ " <td>237736</td>\n",
+ " <td>30.0708</td>\n",
+ " <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>342 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass \\\n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "8 9 1 3 \n",
- "9 10 1 2 \n",
- ".. ... ... ... \n",
- "875 876 1 3 \n",
- "879 880 1 1 \n",
- "880 881 1 2 \n",
- "887 888 1 1 \n",
- "889 890 1 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
- "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
- ".. ... ... ... ... \n",
- "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n",
- "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n",
- "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n",
- "887 Graham, Miss. Margaret Edith female 19.0 0 \n",
- "889 Behr, Mr. Karl Howell male 26.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "8 2 347742 11.1333 NaN S \n",
- "9 0 237736 30.0708 NaN C \n",
- ".. ... ... ... ... ... \n",
- "875 0 2667 7.2250 NaN C \n",
- "879 1 11767 83.1583 C50 C \n",
- "880 1 230433 26.0000 NaN S \n",
- "887 0 112053 30.0000 B42 S \n",
- "889 0 111369 30.0000 C148 C \n",
- "\n",
- "[342 rows x 12 columns]"
+ " PassengerId Survived Pclass \\\n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C "
]
},
- "execution_count": 245,
+ "execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.query(\"Survived == 1\")"
+ "titanic.query(\"Survived == 1\").head()"
]
},
{
@@ -5208,7 +5336,7 @@
},
{
"cell_type": "code",
- "execution_count": 246,
+ "execution_count": 147,
"id": "foster-customs",
"metadata": {},
"outputs": [
@@ -5323,151 +5451,40 @@
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>874</th>\n",
- " <td>875</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
- " <td>female</td>\n",
- " <td>28.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>P/PP 3381</td>\n",
- " <td>24.0000</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>875</th>\n",
- " <td>876</td>\n",
- " <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
- " <td>female</td>\n",
- " <td>15.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2667</td>\n",
- " <td>7.2250</td>\n",
- " <td>NaN</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>879</th>\n",
- " <td>880</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
- " <td>female</td>\n",
- " <td>56.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>11767</td>\n",
- " <td>83.1583</td>\n",
- " <td>C50</td>\n",
- " <td>C</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>880</th>\n",
- " <td>881</td>\n",
- " <td>1</td>\n",
- " <td>2</td>\n",
- " <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
- " <td>female</td>\n",
- " <td>25.0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>230433</td>\n",
- " <td>26.0000</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>887</th>\n",
- " <td>888</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>Graham, Miss. Margaret Edith</td>\n",
- " <td>female</td>\n",
- " <td>19.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>112053</td>\n",
- " <td>30.0000</td>\n",
- " <td>B42</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>233 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass \\\n",
- "1 2 1 1 \n",
- "2 3 1 3 \n",
- "3 4 1 1 \n",
- "8 9 1 3 \n",
- "9 10 1 2 \n",
- ".. ... ... ... \n",
- "874 875 1 2 \n",
- "875 876 1 3 \n",
- "879 880 1 1 \n",
- "880 881 1 2 \n",
- "887 888 1 1 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
- "2 Heikkinen, Miss. Laina female 26.0 0 \n",
- "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
- "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
- "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
- ".. ... ... ... ... \n",
- "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n",
- "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n",
- "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n",
- "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n",
- "887 Graham, Miss. Margaret Edith female 19.0 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "1 0 PC 17599 71.2833 C85 C \n",
- "2 0 STON/O2. 3101282 7.9250 NaN S \n",
- "3 0 113803 53.1000 C123 S \n",
- "8 2 347742 11.1333 NaN S \n",
- "9 0 237736 30.0708 NaN C \n",
- ".. ... ... ... ... ... \n",
- "874 0 P/PP 3381 24.0000 NaN C \n",
- "875 0 2667 7.2250 NaN C \n",
- "879 1 11767 83.1583 C50 C \n",
- "880 1 230433 26.0000 NaN S \n",
- "887 0 112053 30.0000 B42 S \n",
- "\n",
- "[233 rows x 12 columns]"
+ " PassengerId Survived Pclass \\\n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "8 9 1 3 \n",
+ "9 10 1 2 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
+ "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "8 2 347742 11.1333 NaN S \n",
+ "9 0 237736 30.0708 NaN C "
]
},
- "execution_count": 246,
+ "execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "titanic.query(\"Survived == 1 & Sex == 'female'\")"
+ "titanic.query(\"Survived == 1 & Sex == 'female'\").head()"
]
},
{
@@ -5480,37 +5497,32 @@
},
{
"cell_type": "code",
- "execution_count": 247,
+ "execution_count": 151,
"id": "eligible-breath",
"metadata": {},
"outputs": [],
"source": [
- "vips = titanic.Name.sample(10)"
+ "vips = titanic.Name.sample(5)"
]
},
{
"cell_type": "code",
- "execution_count": 248,
+ "execution_count": 152,
"id": "alpine-residence",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "674 Watson, Mr. Ennis Hastings\n",
- "623 Hansen, Mr. Henry Damsgaard\n",
- "62 Harris, Mr. Henry Birkhardt\n",
- "692 Lam, Mr. Ali\n",
- "137 Futrelle, Mr. Jacques Heath\n",
- "165 Goldsmith, Master. Frank John William \"Frankie\"\n",
- "261 Asplund, Master. Edvin Rojj Felix\n",
- "201 Sage, Mr. Frederick\n",
- "335 Denkoff, Mr. Mitto\n",
- "676 Sawyer, Mr. Frederick Charles\n",
+ "24 Palsson, Miss. Torborg Danira\n",
+ "200 Vande Walle, Mr. Nestor Cyriel\n",
+ "630 Barkworth, Mr. Algernon Henry Wilson\n",
+ "80 Waelens, Mr. Achille\n",
+ "410 Sdycoff, Mr. Todor\n",
"Name: Name, dtype: object"
]
},
- "execution_count": 248,
+ "execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
@@ -5521,7 +5533,7 @@
},
{
"cell_type": "code",
- "execution_count": 249,
+ "execution_count": 153,
"id": "therapeutic-sudan",
"metadata": {},
"outputs": [
@@ -5562,153 +5574,78 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>62</th>\n",
- " <td>63</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>Harris, Mr. Henry Birkhardt</td>\n",
- " <td>male</td>\n",
- " <td>45.0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>36973</td>\n",
- " <td>83.4750</td>\n",
- " <td>C83</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>137</th>\n",
- " <td>138</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>Futrelle, Mr. Jacques Heath</td>\n",
- " <td>male</td>\n",
- " <td>37.0</td>\n",
- " <td>1</td>\n",
+ " <th>24</th>\n",
+ " <td>25</td>\n",
" <td>0</td>\n",
- " <td>113803</td>\n",
- " <td>53.1000</td>\n",
- " <td>C123</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>165</th>\n",
- " <td>166</td>\n",
- " <td>1</td>\n",
" <td>3</td>\n",
- " <td>Goldsmith, Master. Frank John William \"Frankie\"</td>\n",
- " <td>male</td>\n",
- " <td>9.0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>363291</td>\n",
- " <td>20.5250</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>201</th>\n",
- " <td>202</td>\n",
- " <td>0</td>\n",
+ " <td>Palsson, Miss. Torborg Danira</td>\n",
+ " <td>female</td>\n",
+ " <td>8.0</td>\n",
" <td>3</td>\n",
- " <td>Sage, Mr. Frederick</td>\n",
- " <td>male</td>\n",
- " <td>NaN</td>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>CA. 2343</td>\n",
- " <td>69.5500</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>261</th>\n",
- " <td>262</td>\n",
" <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Asplund, Master. Edvin Rojj Felix</td>\n",
- " <td>male</td>\n",
- " <td>3.0</td>\n",
- " <td>4</td>\n",
- " <td>2</td>\n",
- " <td>347077</td>\n",
- " <td>31.3875</td>\n",
+ " <td>349909</td>\n",
+ " <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>335</th>\n",
- " <td>336</td>\n",
+ " <th>80</th>\n",
+ " <td>81</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Denkoff, Mr. Mitto</td>\n",
+ " <td>Waelens, Mr. Achille</td>\n",
" <td>male</td>\n",
- " <td>NaN</td>\n",
+ " <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>349225</td>\n",
- " <td>7.8958</td>\n",
+ " <td>345767</td>\n",
+ " <td>9.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>623</th>\n",
- " <td>624</td>\n",
+ " <th>200</th>\n",
+ " <td>201</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Hansen, Mr. Henry Damsgaard</td>\n",
- " <td>male</td>\n",
- " <td>21.0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>350029</td>\n",
- " <td>7.8542</td>\n",
- " <td>NaN</td>\n",
- " <td>S</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>674</th>\n",
- " <td>675</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>Watson, Mr. Ennis Hastings</td>\n",
+ " <td>Vande Walle, Mr. Nestor Cyriel</td>\n",
" <td>male</td>\n",
- " <td>NaN</td>\n",
+ " <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>239856</td>\n",
- " <td>0.0000</td>\n",
+ " <td>345770</td>\n",
+ " <td>9.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>676</th>\n",
- " <td>677</td>\n",
+ " <th>410</th>\n",
+ " <td>411</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
- " <td>Sawyer, Mr. Frederick Charles</td>\n",
+ " <td>Sdycoff, Mr. Todor</td>\n",
" <td>male</td>\n",
- " <td>24.5</td>\n",
+ " <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>342826</td>\n",
- " <td>8.0500</td>\n",
+ " <td>349222</td>\n",
+ " <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>692</th>\n",
- " <td>693</td>\n",
+ " <th>630</th>\n",
+ " <td>631</td>\n",
" <td>1</td>\n",
- " <td>3</td>\n",
- " <td>Lam, Mr. Ali</td>\n",
+ " <td>1</td>\n",
+ " <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
" <td>male</td>\n",
- " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>1601</td>\n",
- " <td>56.4958</td>\n",
- " <td>NaN</td>\n",
+ " <td>27042</td>\n",
+ " <td>30.0000</td>\n",
+ " <td>A23</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
@@ -5716,44 +5653,22 @@
"</div>"
],
"text/plain": [
- " PassengerId Survived Pclass \\\n",
- "62 63 0 1 \n",
- "137 138 0 1 \n",
- "165 166 1 3 \n",
- "201 202 0 3 \n",
- "261 262 1 3 \n",
- "335 336 0 3 \n",
- "623 624 0 3 \n",
- "674 675 0 2 \n",
- "676 677 0 3 \n",
- "692 693 1 3 \n",
- "\n",
- " Name Sex Age SibSp \\\n",
- "62 Harris, Mr. Henry Birkhardt male 45.0 1 \n",
- "137 Futrelle, Mr. Jacques Heath male 37.0 1 \n",
- "165 Goldsmith, Master. Frank John William \"Frankie\" male 9.0 0 \n",
- "201 Sage, Mr. Frederick male NaN 8 \n",
- "261 Asplund, Master. Edvin Rojj Felix male 3.0 4 \n",
- "335 Denkoff, Mr. Mitto male NaN 0 \n",
- "623 Hansen, Mr. Henry Damsgaard male 21.0 0 \n",
- "674 Watson, Mr. Ennis Hastings male NaN 0 \n",
- "676 Sawyer, Mr. Frederick Charles male 24.5 0 \n",
- "692 Lam, Mr. Ali male NaN 0 \n",
- "\n",
- " Parch Ticket Fare Cabin Embarked \n",
- "62 0 36973 83.4750 C83 S \n",
- "137 0 113803 53.1000 C123 S \n",
- "165 2 363291 20.5250 NaN S \n",
- "201 2 CA. 2343 69.5500 NaN S \n",
- "261 2 347077 31.3875 NaN S \n",
- "335 0 349225 7.8958 NaN S \n",
- "623 0 350029 7.8542 NaN S \n",
- "674 0 239856 0.0000 NaN S \n",
- "676 0 342826 8.0500 NaN S \n",
- "692 0 1601 56.4958 NaN S "
+ " PassengerId Survived Pclass Name \\\n",
+ "24 25 0 3 Palsson, Miss. Torborg Danira \n",
+ "80 81 0 3 Waelens, Mr. Achille \n",
+ "200 201 0 3 Vande Walle, Mr. Nestor Cyriel \n",
+ "410 411 0 3 Sdycoff, Mr. Todor \n",
+ "630 631 1 1 Barkworth, Mr. Algernon Henry Wilson \n",
+ "\n",
+ " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "24 female 8.0 3 1 349909 21.0750 NaN S \n",
+ "80 male 22.0 0 0 345767 9.0000 NaN S \n",
+ "200 male 28.0 0 0 345770 9.5000 NaN S \n",
+ "410 male NaN 0 0 349222 7.8958 NaN S \n",
+ "630 male 80.0 0 0 27042 30.0000 A23 S "
]
},
- "execution_count": 249,
+ "execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
@@ -5769,14 +5684,14 @@
"source": [
"### drop_duplicates\n",
"\n",
- "Return DataFrame with duplicate rows removed.\n",
+ "Return DataFrame with duplicated rows removed.\n",
"\n",
"> https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html"
]
},
{
"cell_type": "code",
- "execution_count": 250,
+ "execution_count": 154,
"id": "extended-usage",
"metadata": {},
"outputs": [
@@ -5850,7 +5765,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 250,
+ "execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
@@ -5874,7 +5789,7 @@
},
{
"cell_type": "code",
- "execution_count": 251,
+ "execution_count": 155,
"id": "administrative-partition",
"metadata": {},
"outputs": [
@@ -5941,7 +5856,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 251,
+ "execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
@@ -5960,7 +5875,7 @@
},
{
"cell_type": "code",
- "execution_count": 252,
+ "execution_count": 156,
"id": "english-parallel",
"metadata": {},
"outputs": [
@@ -6013,7 +5928,7 @@
"2 Indomie cup 3.5"
]
},
- "execution_count": 252,
+ "execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
@@ -6032,7 +5947,7 @@
},
{
"cell_type": "code",
- "execution_count": 253,
+ "execution_count": 157,
"id": "corresponding-owner",
"metadata": {},
"outputs": [
@@ -6092,7 +6007,7 @@
"4 Indomie pack 5.0"
]
},
- "execution_count": 253,
+ "execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
@@ -6111,7 +6026,7 @@
},
{
"cell_type": "code",
- "execution_count": 254,
+ "execution_count": 160,
"id": "serial-omaha",
"metadata": {},
"outputs": [
@@ -6170,12 +6085,12 @@
"source": [
"for p_class, df in titanic.groupby('Pclass'):\n",
" print(f\"##################### {p_class} #########################\")\n",
- " print(df[['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age']])"
+ " print(df.loc[:,['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age']])"
]
},
{
"cell_type": "code",
- "execution_count": 255,
+ "execution_count": 161,
"id": "exclusive-madison",
"metadata": {},
"outputs": [
@@ -6254,7 +6169,7 @@
" print(f\"##################### {p_class} #########################\")\n",
" for sex, df2 in df.groupby('Sex'):\n",
" print(f\"================== {sex} =================\")\n",
- " print(df2[['Survived', 'Age']].describe())"
+ " print(df2.loc[:,['Survived', 'Age']].describe())"
]
},
{
@@ -6270,7 +6185,7 @@
},
{
"cell_type": "code",
- "execution_count": 256,
+ "execution_count": 162,
"id": "institutional-promotion",
"metadata": {},
"outputs": [],
@@ -6283,7 +6198,7 @@
},
{
"cell_type": "code",
- "execution_count": 257,
+ "execution_count": 163,
"id": "upset-joyce",
"metadata": {},
"outputs": [
@@ -6339,7 +6254,7 @@
"2 3 HORSE"
]
},
- "execution_count": 257,
+ "execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
@@ -6350,7 +6265,7 @@
},
{
"cell_type": "code",
- "execution_count": 258,
+ "execution_count": 164,
"id": "hidden-attitude",
"metadata": {},
"outputs": [
@@ -6406,7 +6321,7 @@
"2 1 45"
]
},
- "execution_count": 258,
+ "execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
@@ -6415,9 +6330,17 @@
"table_2"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "bf5878f4-ea10-4f8d-90da-ef5fd25154ea",
+ "metadata": {},
+ "source": [
+ "You can merge table specifying the colonne use as keys for the merge:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 259,
+ "execution_count": 165,
"id": "separated-extreme",
"metadata": {},
"outputs": [
@@ -6477,7 +6400,7 @@
"2 3 HORSE 33"
]
},
- "execution_count": 259,
+ "execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
@@ -6488,7 +6411,7 @@
},
{
"cell_type": "code",
- "execution_count": 260,
+ "execution_count": 168,
"id": "impressed-copper",
"metadata": {},
"outputs": [
@@ -6544,7 +6467,7 @@
"2 1 45"
]
},
- "execution_count": 260,
+ "execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
@@ -6555,9 +6478,17 @@
"table_3"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "7b77bd7e-2dc7-48ba-9817-d6eb7bb01df4",
+ "metadata": {},
+ "source": [
+ "If tables to merge got different name of columns for the keys to use, you can use the `left_on`, `right_on` arguments:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 261,
+ "execution_count": 167,
"id": "identified-posting",
"metadata": {},
"outputs": [
@@ -6621,7 +6552,7 @@
"2 3 HORSE 3 33"
]
},
- "execution_count": 261,
+ "execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
@@ -6635,12 +6566,12 @@
"id": "homeless-arlington",
"metadata": {},
"source": [
- "### Effect of *how* parameter"
+ "### Effect of the *how* parameter"
]
},
{
"cell_type": "code",
- "execution_count": 262,
+ "execution_count": 169,
"id": "logical-alfred",
"metadata": {},
"outputs": [
@@ -6666,7 +6597,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gene_ID</th>\n",
- " <th>specie</th>\n",
+ " <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -6695,27 +6626,27 @@
"</div>"
],
"text/plain": [
- " gene_ID specie\n",
+ " gene_ID species\n",
"0 1 HUMAN\n",
"1 12 RAT\n",
"2 3 HORSE\n",
"3 42 MONKEY"
]
},
- "execution_count": 262,
+ "execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_4 = pd.DataFrame({'gene_ID':[1,12,3, 42],\n",
- " 'specie': ['HUMAN', 'RAT', 'HORSE', 'MONKEY']})\n",
+ " 'species': ['HUMAN', 'RAT', 'HORSE', 'MONKEY']})\n",
"table_4"
]
},
{
"cell_type": "code",
- "execution_count": 263,
+ "execution_count": 170,
"id": "progressive-blogger",
"metadata": {},
"outputs": [
@@ -6777,7 +6708,7 @@
"3 35 100"
]
},
- "execution_count": 263,
+ "execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
@@ -6788,9 +6719,19 @@
"table_5"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "bd953fa8-3734-4bf4-a358-a97caa65c6ee",
+ "metadata": {},
+ "source": [
+ "Here are the different ways of merging tables using the `how` argument. In case no values are present, NaN will be added.\n",
+ "\n",
+ "All table_4 (the *left* table) rows will be kept: "
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 264,
+ "execution_count": 171,
"id": "stock-attachment",
"metadata": {},
"outputs": [
@@ -6816,7 +6757,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gene_ID</th>\n",
- " <th>specie</th>\n",
+ " <th>species</th>\n",
" <th>ref</th>\n",
" <th>effect</th>\n",
" </tr>\n",
@@ -6855,14 +6796,14 @@
"</div>"
],
"text/plain": [
- " gene_ID specie ref effect\n",
+ " gene_ID species ref effect\n",
"0 1 HUMAN 1.0 45.0\n",
"1 12 RAT 12.0 12.0\n",
"2 3 HORSE 3.0 33.0\n",
"3 42 MONKEY NaN NaN"
]
},
- "execution_count": 264,
+ "execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
@@ -6871,9 +6812,17 @@
"pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='left')"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "bdb22596-034c-4eca-9f39-851accb99d83",
+ "metadata": {},
+ "source": [
+ "All table_5 (the *right* table) rows will be kept: "
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 265,
+ "execution_count": 172,
"id": "equivalent-conservative",
"metadata": {},
"outputs": [
@@ -6899,7 +6848,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gene_ID</th>\n",
- " <th>specie</th>\n",
+ " <th>species</th>\n",
" <th>ref</th>\n",
" <th>effect</th>\n",
" </tr>\n",
@@ -6938,14 +6887,14 @@
"</div>"
],
"text/plain": [
- " gene_ID specie ref effect\n",
- "0 12.0 RAT 12 12\n",
- "1 3.0 HORSE 3 33\n",
- "2 1.0 HUMAN 1 45\n",
- "3 NaN NaN 35 100"
+ " gene_ID species ref effect\n",
+ "0 12.0 RAT 12 12\n",
+ "1 3.0 HORSE 3 33\n",
+ "2 1.0 HUMAN 1 45\n",
+ "3 NaN NaN 35 100"
]
},
- "execution_count": 265,
+ "execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
@@ -6954,9 +6903,17 @@
"pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='right')"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "77936c7b-69ed-4e95-b523-87406c44c298",
+ "metadata": {},
+ "source": [
+ "Only common rows will be kept:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 266,
+ "execution_count": 173,
"id": "seasonal-publisher",
"metadata": {},
"outputs": [
@@ -6982,7 +6939,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gene_ID</th>\n",
- " <th>specie</th>\n",
+ " <th>species</th>\n",
" <th>ref</th>\n",
" <th>effect</th>\n",
" </tr>\n",
@@ -7014,13 +6971,13 @@
"</div>"
],
"text/plain": [
- " gene_ID specie ref effect\n",
- "0 1 HUMAN 1 45\n",
- "1 12 RAT 12 12\n",
- "2 3 HORSE 3 33"
+ " gene_ID species ref effect\n",
+ "0 1 HUMAN 1 45\n",
+ "1 12 RAT 12 12\n",
+ "2 3 HORSE 3 33"
]
},
- "execution_count": 266,
+ "execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
@@ -7029,9 +6986,17 @@
"pd.merge(table_4, table_5, left_on='gene_ID', right_on='ref', how='inner')"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "514f0885-8cad-4182-9c34-c7da0399b498",
+ "metadata": {},
+ "source": [
+ "All rows will be kept:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 267,
+ "execution_count": 174,
"id": "neural-christianity",
"metadata": {},
"outputs": [
@@ -7057,7 +7022,7 @@
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gene_ID</th>\n",
- " <th>specie</th>\n",
+ " <th>species</th>\n",
" <th>ref</th>\n",
" <th>effect</th>\n",
" </tr>\n",
@@ -7103,7 +7068,7 @@
"</div>"
],
"text/plain": [
- " gene_ID specie ref effect\n",
+ " gene_ID species ref effect\n",
"0 1.0 HUMAN 1.0 45.0\n",
"1 12.0 RAT 12.0 12.0\n",
"2 3.0 HORSE 3.0 33.0\n",
@@ -7111,7 +7076,7 @@
"4 NaN NaN 35.0 100.0"
]
},
- "execution_count": 267,
+ "execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
@@ -7127,14 +7092,14 @@
"source": [
"## Crosstab\n",
"\n",
- "Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factors \n",
+ "Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factor.\n",
"\n",
"> https://pandas.pydata.org/docs/reference/api/pandas.crosstab.html"
]
},
{
"cell_type": "code",
- "execution_count": 268,
+ "execution_count": 181,
"id": "appropriate-astrology",
"metadata": {},
"outputs": [
@@ -7260,7 +7225,7 @@
"[88 rows x 3 columns]"
]
},
- "execution_count": 268,
+ "execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
@@ -7276,7 +7241,13 @@
"source": [
"## Saving data\n",
"\n",
- "To **csv** or **tsv** files:\n",
+ "To **csv**:\n",
+ "\n",
+ "```python\n",
+ "df.to_csv(<path to file>, index=False)\n",
+ "```\n",
+ "\n",
+ "or **tsv** files:\n",
"\n",
"```python\n",
"df.to_csv(<path to file>, sep='\\t', index=False)\n",
@@ -7313,7 +7284,7 @@
},
{
"cell_type": "code",
- "execution_count": 269,
+ "execution_count": 185,
"id": "corresponding-natural",
"metadata": {},
"outputs": [
@@ -7366,7 +7337,7 @@
"1 4 42 6"
]
},
- "execution_count": 269,
+ "execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
@@ -7388,7 +7359,7 @@
},
{
"cell_type": "code",
- "execution_count": 270,
+ "execution_count": 186,
"id": "stunning-retrieval",
"metadata": {},
"outputs": [
@@ -7441,7 +7412,7 @@
"1 4 5 6"
]
},
- "execution_count": 270,
+ "execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
@@ -7465,7 +7436,7 @@
},
{
"cell_type": "code",
- "execution_count": 271,
+ "execution_count": 187,
"id": "relevant-sentence",
"metadata": {},
"outputs": [
@@ -7475,13 +7446,13 @@
"<AxesSubplot:>"
]
},
- "execution_count": 271,
+ "execution_count": 187,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAARC0lEQVR4nO3dX4xcZ33G8e+PlDbGC7HdJCvXQTWoVgrKNgFvQ2iqajemyCQI56KJggAZKZUvCjRUrqjTSq24qOqLBtELVNUCGqulWVJIGiuVgGjJFKWqCmsI2KmThhLLxEltSG3DuhHF4deLPYuHze78n53z2t+PtJo5Z8+ZeTI7fvLuO+ecjcxEklSeV4w6gCSpNxa4JBXKApekQlngklQoC1ySCvVzq/lkl19+eW7evLnr/c6ePcvatWsHH6hP5upOXXNBfbOZqzt1zQX9ZTt48OD3M/OKl30jM1fta+vWrdmLRx99tKf9hs1c3alrrsz6ZjNXd+qaK7O/bMBcLtOpTqFIUqEscEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhVvVUeq2ezXv+ueX3j+69ZZWSSBoWR+CSVCgLXJIKZYFLUqEscEkqVEcfYkbEUeCHwEvAucycjIgNwGeBzcBR4PbMPDWcmJKkpboZgU9n5nWZOVkt7wFmM3MLMFstS5JWST9TKDuA/dX9/cCtfaeRJHUsFv7YQ5uNIp4BTgEJ/E1m7ouI05m5rmmbU5m5fpl9dwG7AMbHx7fOzMx0HXJ+fp6xsbGu9xu2Oud65sxLLbeZ2HTZKqU5r66vF9Q3m7m6U9dc0F+26enpg02zHz/V6Yk8N2bmcxFxJfBIRDzZ6RNn5j5gH8Dk5GROTU11uutPNRoNetlv2Oqc657Hzrbc5uh7plYnTJO6vl5Q32zm6k5dc8FwsnU0hZKZz1W3J4EHgeuBExGxEaC6PTnQZJKkltoWeESsjYhXL94H3g4cBg4AO6vNdgIPDSukJOnlOplCGQcejIjF7f8hM78QEV8D7o+IO4FjwG3DiylJWqptgWfmd4Brl1n/ArBtGKEkSe15JqYkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlH8TUwO30t/j3D1xjqnVjSJd0ByBS1KhLHBJKpQFLkmFcg78IrXSPDXA0b23rGISSb1yBC5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKa6Goa62uoyJp9TgCl6RCWeCSVCgLXJIK5Ry4XsY5bqkMjsAlqVAWuCQVygKXpEJ1PAceEZcAc8DxzHxnRGwAPgtsBo4Ct2fmqWGElBb5tzyl87oZgd8FHGla3gPMZuYWYLZaliStko4KPCKuAm4BPtm0egewv7q/H7h1oMkkSS11OgL/OPAR4CdN68Yz83mA6vbKwUaTJLUSmdl6g4h3Ajdn5u9FxBTwh9Uc+OnMXNe03anMXL/M/ruAXQDj4+NbZ2Zmug45Pz/P2NhY1/sN26hzHTp+Ztn142vgxIurHKYD42vgyg2X9fUYK/03A0xs6v2xR/2zXIm5ulPXXNBftunp6YOZObl0fScF/hfA+4BzwKXAa4AHgF8HpjLz+YjYCDQy8+pWjzU5OZlzc3Ndh280GkxNTXW937CNOtdKH+jtnjjHPYfqd47W7olzfOg9O/p6jGF9iDnqn+VKzNWduuaC/rJFxLIF3nYKJTPvzsyrMnMzcAfw5cx8L3AA2FltthN4qKdkkqSe9DNM2wvcHxF3AseA2wYTSYs8pV1SK10VeGY2gEZ1/wVg2+AjSZI64ZmYklQoC1ySCmWBS1KhLHBJKpQFLkmFssAlqVD1O11PF7R2x7Z7SVipc47AJalQFrgkFcoCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKpQFLkmFssAlqVAWuCQVygKXpEL5NzFVK+3+Zqak8xyBS1KhLHBJKpQFLkmFssAlqVBtCzwiLo2Ir0bENyPiiYj4aLV+Q0Q8EhFPV7frhx9XkrSokxH4j4CbMvNa4Dpge0TcAOwBZjNzCzBbLUuSVknbAs8F89XiK6uvBHYA+6v1+4FbhxFQkrS8yMz2G0VcAhwEfgX4RGb+UUSczsx1TducysyXTaNExC5gF8D4+PjWmZmZrkPOz88zNjbW9X7DNuxch46f6Wm/8TVw4sUBhxmAYeea2HRZz/terO+xXpmre/1km56ePpiZk0vXd1TgP904Yh3wIPAh4LFOCrzZ5ORkzs3Ndfx8ixqNBlNTU13vN2zDztXrSS27J85xz6H6naM17FxH997S874X63usV+bqXj/ZImLZAu/qKJTMPA00gO3AiYjYWD34RuBkT8kkST3p5CiUK6qRNxGxBngb8CRwANhZbbYTeGhIGSVJy+jk99mNwP5qHvwVwP2Z+XBE/Btwf0TcCRwDbhtiTknSEm0LPDO/BbxpmfUvANuGEUqS1J5nYkpSoSxwSSqUBS5JhbLAJalQFrgkFcoCl6RC1e98a2lIWl2a4N7ta1cxiTQYjsAlqVAWuCQVygKXpEJZ4JJUKAtckgplgUtSoSxwSSqUBS5JhbLAJalQFrgkFcoCl6RCFXMtlFbXsQA4uveWVUoiSfXgCFySCmWBS1KhLHBJKlQxc+AXonbz+pLUiiNwSSqUBS5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIK5XHgEnDo+Bne3+K4fK+1ozpqOwKPiNdGxKMRcSQinoiIu6r1GyLikYh4urpdP/y4kqRFnUyhnAN2Z+YbgBuAD0TEG4E9wGxmbgFmq2VJ0ippW+CZ+Xxmfr26/0PgCLAJ2AHsrzbbD9w6pIySpGVEZna+ccRm4CvANcCxzFzX9L1TmfmyaZSI2AXsAhgfH986MzPTdcj5+XmeOfNSy20mNl3W9eP2a35+nrGxsZ73P3T8zADTnDe+Bk68OJSH7suwc7V7D7R6vdtlG8X7C/p/jw2LubrXT7bp6emDmTm5dH3HBR4RY8C/AH+emQ9ExOlOCrzZ5ORkzs3NdZccaDQavP8LZ1tuM4oPmRqNBlNTUz3vP6yLWe2eOMc9h+r3+fSwc7V7D7R6vdtlG9WHmP2+x4bFXN3rJ1tELFvgHR1GGBGvBD4PfCYzH6hWn4iIjdX3NwIne0omSepJJ0ehBPAp4EhmfqzpWweAndX9ncBDg48nSVpJJ7/P3gi8DzgUEY9X6/4Y2AvcHxF3AseA24aSUJK0rLYFnpmPAbHCt7cNNo4kqVOeSi9JhbLAJalQ9TvWTOpRXf/GaLtcXmdFvXIELkmFssAlqVAWuCQVyjlwaQDqOv+uC5sjcEkqlAUuSYWywCWpUBfMHHirOUiPs1WdtXrv3rt97SomUWkcgUtSoSxwSSqUBS5JhbLAJalQFrgkFcoCl6RCWeCSVKgL5jhwaZi81onqyBG4JBXKApekQlngklQo58ClGjt0/Azv9zo/WoEjcEkqlAUuSYWywCWpUBa4JBXKApekQlngklQoC1ySCmWBS1Kh2hZ4RHw6Ik5GxOGmdRsi4pGIeLq6XT/cmJKkpToZgd8LbF+ybg8wm5lbgNlqWZK0itoWeGZ+BfifJat3APur+/uBWwcbS5LUTmRm+40iNgMPZ+Y11fLpzFzX9P1TmbnsNEpE7AJ2AYyPj2+dmZnpOuT8/DzPnHmp6/0WTWy6rOd9YeF6FMsZXwNXbuj9sVd63H6Nr4ETLw7loftS11xQ32ztcvX73u7V/Pw8Y2NjI3nuVuqaC/rLNj09fTAzJ5euH/rFrDJzH7APYHJyMqemprp+jEajwT2Pne05w9H3dP+czVa6mNDuiXPc3sN/T7vH7dfuiXPcc6h+1ymray6ob7Z2ufp9b/eq0WjQy7/lYatrLhhOtl6PQjkRERsBqtuTg4skSepErwV+ANhZ3d8JPDSYOJKkTrX9nTEi7gOmgMsj4lngz4C9wP0RcSdwDLhtmCElLa/d3+r0euEXtrYFnpnvXuFb2wacRZLUBc/ElKRCWeCSVKj6HTd1gWk3RylJvXIELkmFssAlqVAWuCQV6qKYA3ceWherft77rY4hP3T8TMtLQXj8+epwBC5JhbLAJalQFrgkFeqimAOX1L1W8+e7J1YxiFbkCFySCmWBS1KhLHBJKpRz4H3yGHNJo+IIXJIKZYFLUqEscEkqlHPgkgZuWNdg0c9yBC5JhbLAJalQFrgkFco5cEkXjIvtOuWOwCWpUBa4JBXKApekQjkHLqkoXqf8PEfgklQoC1ySCmWBS1KhnAOXdNFod42Wfo4Tb/fY925f2/Njr6SvEXhEbI+IpyLi2xGxZ1ChJEnt9VzgEXEJ8AngHcAbgXdHxBsHFUyS1Fo/I/DrgW9n5ncy8/+AGWDHYGJJktqJzOxtx4jfAbZn5u9Wy+8D3pKZH1yy3S5gV7V4NfBUD093OfD9noIOl7m6U9dcUN9s5upOXXNBf9l+OTOvWLqynw8xY5l1L/u/QWbuA/b18TxExFxmTvbzGMNgru7UNRfUN5u5ulPXXDCcbP1MoTwLvLZp+Srguf7iSJI61U+Bfw3YEhGvi4ifB+4ADgwmliSpnZ6nUDLzXER8EPgicAnw6cx8YmDJflZfUzBDZK7u1DUX1DebubpT11wwhGw9f4gpSRotT6WXpEJZ4JJUqFoXeJ1O1Y+IT0fEyYg43LRuQ0Q8EhFPV7frVznTayPi0Yg4EhFPRMRddchVZbg0Ir4aEd+ssn20LtmqHJdExDci4uG65IqIoxFxKCIej4i5GuVaFxGfi4gnq/faW2uS6+rqtVr8+kFEfLgm2f6get8fjoj7qn8PA89V2wKv4an69wLbl6zbA8xm5hZgtlpeTeeA3Zn5BuAG4APVazTqXAA/Am7KzGuB64DtEXFDTbIB3AUcaVquS67pzLyu6XjhOuT6K+ALmfmrwLUsvG4jz5WZT1Wv1XXAVuB/gQdHnS0iNgG/D0xm5jUsHORxx1ByZWYtv4C3Al9sWr4buHvEmTYDh5uWnwI2Vvc3Ak+NON9DwG/XMNergK8Db6lDNhbOWZgFbgIersvPEjgKXL5k3UhzAa8BnqE64KEuuZbJ+XbgX+uQDdgEfBfYwMKRfg9X+Qaeq7YjcM6/CIuerdbVyXhmPg9Q3V45qiARsRl4E/DvdclVTVM8DpwEHsnMumT7OPAR4CdN6+qQK4EvRcTB6hIUdcj1euB7wN9WU06fjIi1Nci11B3AfdX9kWbLzOPAXwLHgOeBM5n5pWHkqnOBd3SqviAixoDPAx/OzB+MOs+izHwpF369vQq4PiKuGXEkIuKdwMnMPDjqLMu4MTPfzMK04Qci4rdGHYiFEeSbgb/OzDcBZxnd9NKyqhMJ3wX846izAFRz2zuA1wG/BKyNiPcO47nqXOAlnKp/IiI2AlS3J1c7QES8koXy/kxmPlCXXM0y8zTQYOEzhFFnuxF4V0QcZeEKmjdFxN/XIBeZ+Vx1e5KFudzra5DrWeDZ6rcngM+xUOijztXsHcDXM/NEtTzqbG8DnsnM72Xmj4EHgN8YRq46F3gJp+ofAHZW93eyMAe9aiIigE8BRzLzY3XJVWW7IiLWVffXsPCmfnLU2TLz7sy8KjM3s/Ce+nJmvnfUuSJibUS8evE+C3Omh0edKzP/G/huRFxdrdoG/Meocy3xbs5Pn8Dosx0DboiIV1X/Rrex8MHv4HON8oOHDj4MuBn4T+C/gD8ZcZb7WJjP+jELo5I7gV9k4cOwp6vbDauc6TdZmFb6FvB49XXzqHNV2X4N+EaV7TDwp9X6kWdryjjF+Q8xR/2zfD3wzerricX3+6hzVRmuA+aqn+U/AevrkKvK9irgBeCypnUjzwZ8lIUBy2Hg74BfGEYuT6WXpELVeQpFktSCBS5JhbLAJalQFrgkFcoCl6RCWeCSVCgLXJIK9f+G8SKnugrwJQAAAABJRU5ErkJggg==\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -7517,9 +7488,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "dev",
+ "display_name": "Python [conda env:dev]",
"language": "python",
- "name": "dev"
+ "name": "conda-env-dev-py"
},
"language_info": {
"codemirror_mode": {
@@ -7531,7 +7502,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.10.4"
}
},
"nbformat": 4,