update seaborn part

1c08b33b · Etienne Kornobis · 210e8a02 · 1c08b33b · 1c08b33b
Commit 1c08b33b authored 2 years ago by Etienne Kornobis
--- a/notebooks/seaborn_TP_solutions.ipynb
+++ b/notebooks/seaborn_TP_solutions.ipynb
@@ -29,7 +29,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
   "id": "minor-doctrine",
   "metadata": {},
   "outputs": [],
@@ -40,7 +40,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
   "id": "skilled-daniel",
   "metadata": {},
   "outputs": [],
@@ -311,7 +311,7 @@
   "id": "portuguese-worse",
   "metadata": {},
   "source": [
-    "- Using an histogram, display the distribution of age in the dataset"
+    "- Using an histogram and continuous probability density curve, display the distribution of age in the dataset"
   ]
  },
  {
@@ -473,9 +473,9 @@
 ],
 "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": {
@@ -487,7 +487,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.4"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id:instrumental-personal tags:
 # <center>**TP**</center>
 <div style="text-align:center">
    <img src="images/seaborn.png" width="600px">
    <div>
       Bertrand Néron, François Laurent, Etienne Kornobis
       <br />
       <a src=" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/">Bioinformatics and Biostatistiqucs HUB</a>
       <br />
       © Institut Pasteur, 2021
    </div>
 </div>
 %% Cell type:markdown id:compliant-basis tags:
 Practice your graphing skills using data from milieu intérieur in `data/mi.csv`:
 %% Cell type:code id:minor-doctrine tags:
 ``` python
 import pandas as pd
 import seaborn as sns
 ```
 %% Cell type:code id:skilled-daniel tags:
 ``` python
 mi_df = pd.read_csv("data/mi.csv")
 ```
 %% Cell type:code id:brutal-manufacturer tags:
 ``` python
 mi_df.head()
 ```
 %% Output
       Unnamed: 0    Age OwnsHouse  PhysicalActivity     Sex LivesWithPartner  \
    0           1  22.33       Yes               3.0  Female               No
    1           2  28.83       Yes               0.0  Female              Yes
    2           3  23.67       Yes               0.0  Female              Yes
    3           4  21.17        No               0.5  Female               No
    4           5  26.17       Yes               1.5  Female               No
      LivesWithKids BornInCity  Inbreeding    BMI  ... VaccineWhoopingCough  \
    0            No        Yes     94.9627  20.13  ...                  Yes
    1            No        Yes     79.1024  21.33  ...                  Yes
    2            No        Yes    117.2540  22.18  ...                   No
    3            No         No     94.1796  18.68  ...                   No
    4            No        Yes    105.1250  29.01  ...                  Yes
       VaccineYellowFever  VaccineHepB  VaccineFlu  SUBJID  DepressionScore  \
    0                  No          Yes          No       2              0.0
    1                  No          Yes          No       3              0.0
    2                  No          Yes          No       4              0.0
    3                  No          Yes          No       5              1.0
    4                  No          Yes          No       8              0.0
       HeartRate  Temperature HourOfSampling DayOfSampling
    0         66         36.8          8.883            40
    1         66         37.4          9.350            40
    2         62         36.9          8.667            40
    3         64         36.0          9.883            40
    4         67         36.7          8.550            81
    [5 rows x 44 columns]
 %% Cell type:markdown id:departmental-exhibition tags:
 - Do a boxplot showing the differences in temperature between females and males:
 %% Cell type:code id:saved-identity tags:
 ``` python
 sns.boxplot(data=mi_df, x="Sex", y="Temperature")
 ```
 %% Output
    <AxesSubplot:xlabel='Sex', ylabel='Temperature'>
 %% Cell type:markdown id:portuguese-worse tags:
- Using an histogram, display the distribution of age in the dataset
+- Using an histogram and continuous probability density curve, display the distribution of age in the dataset
 %% Cell type:code id:continuous-indian tags:
 ``` python
 sns.histplot(data=mi_df, x="Age")
 ```
 %% Output
    <AxesSubplot:xlabel='Age', ylabel='Count'>
 %% Cell type:code id:understanding-vegetarian tags:
 ``` python
 sns.histplot(data=mi_df, x="Age", kde=True)
 ```
 %% Output
    <AxesSubplot:xlabel='Age', ylabel='Count'>
 %% Cell type:markdown id:prepared-stephen tags:
 - Use a barplot to show the count of vaccinated for yellow fever (see the documentation for a countplot)
 %% Cell type:code id:worldwide-communication tags:
 ``` python
 sns.countplot(data=mi_df, x="VaccineYellowFever")
 ```
 %% Output
    <AxesSubplot:xlabel='VaccineYellowFever', ylabel='count'>
 %% Cell type:markdown id:immediate-method tags:
 - Plot the distribution of age for the people vaccinated for the flu
 %% Cell type:code id:academic-measure tags:
 ``` python
 sns.histplot(data=mi_df.query("VaccineFlu == 'Yes'"), x="Age", kde=True)
 ```
 %% Output
    <AxesSubplot:xlabel='Age', ylabel='Count'>
 %% Cell type:markdown id:temporal-synthesis tags:
 - Feel free to explore more of [seaborn](https://seaborn.pydata.org/examples/index.html) !

--- a/notebooks/seaborn_cours.ipynb
+++ b/notebooks/seaborn_cours.ipynb