Statistique descriptive

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
print("la moyenne d age= " , titanic["Age"].mean())
titanic[["Age", "Fare"]].median()

Output: 

la moyenne d age=  29.69911764705882
Age     28.0000
Fare    14.4542
dtype: float64

Sommaire: 

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
titanic[["Age", "Fare"]].describe()

Output: 

	Age	Fare
count	714.000000	891.000000
mean	29.699118	32.204208
std	14.526497	49.693429
min	0.420000	0.000000
25%	20.125000	7.910400
50%	28.000000	14.454200
75%	38.000000	31.000000
max	80.000000	512.329200

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
titanic.agg(
    {
        "Age": ["min", "max", "median", "skew"],
        "Fare": ["min", "max", "median", "mean"],
    }
)

Output: 

Age	Fare
min	0.420000	0.000000
max	80.000000	512.329200
median	28.000000	14.454200
skew	0.389108	NaN
mean	NaN	32.204208

Count:

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
titanic["Pclass"].value_counts()

Output:

3    491
1    216
2    184
Name: Pclass, dtype: int64

Regroupement:

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
titanic.groupby(["Sex", "Pclass"])["Fare"].mean()

Output:

Sex     Pclass
female  1         106.125798
        2          21.970121
        3          16.118810
male    1          67.226127
        2          19.741782
        3          12.661633
Name: Fare, dtype: float64

 Correlation:

import pandas as pd
titanic  = pd.read_csv('https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv')
titanic.corr()

 Output:

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
PassengerId	1.000000	-0.005007	-0.035144	0.036847	-0.057527	-0.001652	0.012658
Survived	-0.005007	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.035144	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	0.036847	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.057527	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	-0.001652	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.012658	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000