In [11]:
import pandas as pd
import seaborn as sns
In [12]:
df = sns.load_dataset("titanic")
In [13]:
df.head()
Out[13]:
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
In [14]:
sns.barplot(x ="class", y= "age", data = df)
Out[14]:
<Axes: xlabel='class', ylabel='age'>
No description has been provided for this image
In [29]:
sns.barplot(x ="class", y= "age",hue = "sex", data = df)
Out[29]:
<Axes: xlabel='class', ylabel='age'>
No description has been provided for this image
In [6]:
sns.barplot(x ="class", y= "age",hue = "sex",estimator="sum", data = df)
Out[6]:
<Axes: xlabel='class', ylabel='age'>
No description has been provided for this image
In [19]:
sns.boxplot(x = "sex", y= "age", data = df)
Out[19]:
<Axes: xlabel='sex', ylabel='age'>
No description has been provided for this image
In [20]:
sns.boxplot(x = "sex", y= "age", hue = "class", data = df)
Out[20]:
<Axes: xlabel='sex', ylabel='age'>
No description has been provided for this image
In [25]:
sns.histplot(df[df["survived"]==0]["age"],  kde=True, stat="density", linewidth=0)
sns.histplot(df[df["survived"]==1]["age"],   kde=True, stat="density", linewidth=0)
Out[25]:
<Axes: xlabel='age', ylabel='Density'>
No description has been provided for this image
In [26]:
sns.violinplot(x = "sex", y = "fare", data = df)
Out[26]:
<Axes: xlabel='sex', ylabel='fare'>
No description has been provided for this image
In [27]:
df
Out[27]:
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
886 0 2 male 27.0 0 0 13.0000 S Second man True NaN Southampton no True
887 1 1 female 19.0 0 0 30.0000 S First woman False B Southampton yes True
888 0 3 female NaN 1 2 23.4500 S Third woman False NaN Southampton no False
889 1 1 male 26.0 0 0 30.0000 C First man True C Cherbourg yes True
890 0 3 male 32.0 0 0 7.7500 Q Third man True NaN Queenstown no True

891 rows × 15 columns

In [ ]:
sns.s