Corrigé exos série 2¶

In [19]:

import pandas as pd
import matplotlib.pyplot as plt

titanic = pd.read_csv("titanic.csv")
titanic.head()

import pandas as pd import matplotlib.pyplot as plt titanic = pd.read_csv("titanic.csv") titanic.head()

Out[19]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

Afficher l'histogramme du nombre d'hommes et de femmes¶

In [20]:

titanic.Sex.value_counts().plot(kind="bar")

titanic.Sex.value_counts().plot(kind="bar")

Out[20]:

<Axes: xlabel='Sex'>

In [21]:

titanic.Sex.hist()

titanic.Sex.hist()

Out[21]:

<Axes: >

Analyser la répartition des ages des passagers¶

In [22]:

fig, axs = plt.subplots(2)
titanic.Age.hist(ax=axs[0], bins=8)
titanic.Age.plot.box(ax=axs[1])
plt.show()

fig, axs = plt.subplots(2) titanic.Age.hist(ax=axs[0], bins=8) titanic.Age.plot.box(ax=axs[1]) plt.show()

Analyser le lien entre la survie et le sexe¶

In [ ]:

titanic["Did survive"] = ["yes" if x == 0 else "no" for x in titanic["Survived"]]
survive_df = titanic[titanic["Survived"] == 0][["Sex", "Age"]]
survive_df.head()

titanic["Did survive"] = ["yes" if x == 0 else "no" for x in titanic["Survived"]] survive_df = titanic[titanic["Survived"] == 0][["Sex", "Age"]] survive_df.head()

Out[ ]:

	Sex	Age
0	male	22.0
4	male	35.0
5	male	NaN
6	male	54.0
7	male	2.0

In [38]:

fig, axs = plt.subplots(2)
survive_df["Sex"].hist(ax=axs[0], bins=8)
survive_df.plot.box(by="Sex", ax=axs[1])
plt.show()

fig, axs = plt.subplots(2) survive_df["Sex"].hist(ax=axs[0], bins=8) survive_df.plot.box(by="Sex", ax=axs[1]) plt.show()

In [49]:

pct_male = 100 * (1 - titanic.loc[titanic["Sex"] == "male", "Survived"].mean())
pct_female = 100 * (1 - titanic.loc[titanic["Sex"] == "female", "Survived"].mean()) 
pct_survived = {
  "Sex": ["male", "female"],
  "Survived percentage": [pct_male, pct_female],
}
pct_survived

pct_male = 100 * (1 - titanic.loc[titanic["Sex"] == "male", "Survived"].mean()) pct_female = 100 * (1 - titanic.loc[titanic["Sex"] == "female", "Survived"].mean()) pct_survived = { "Sex": ["male", "female"], "Survived percentage": [pct_male, pct_female], } pct_survived

Out[49]:

{'Sex': ['male', 'female'],
 'Survived percentage': [np.float64(81.10918544194108),
  np.float64(25.79617834394905)]}

In [57]:

pct_survived_df = pd.DataFrame(pct_survived)
pct_survived_df.plot.bar(x="Sex")

pct_survived_df = pd.DataFrame(pct_survived) pct_survived_df.plot.bar(x="Sex")

Out[57]:

<Axes: xlabel='Sex'>