In [1]:

    

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


    

In [2]:

    

df = pd.DataFrame({'A': range(5),
                   'B': [x**2 for x in range(5)],
                   'C': [x**3 for x in range(5)]})


    

In [3]:

    

print(df)


    

    




   A   B   C
0  0   0   0
1  1   1   1
2  2   4   8
3  3   9  27
4  4  16  64

In [5]:

    

df_corr = df.corr()
print(df_corr)
print(type(df_corr))


    

    




          A         B         C
A  1.000000  0.958927  0.905882
B  0.958927  1.000000  0.987130
C  0.905882  0.987130  1.000000
<class 'pandas.core.frame.DataFrame'>

In [6]:

    

df['D'] = list('abcde')
df['E'] = [True, False, True, True, False]
print(df)


    

    




   A   B   C  D      E
0  0   0   0  a   True
1  1   1   1  b  False
2  2   4   8  c   True
3  3   9  27  d   True
4  4  16  64  e  False

In [7]:

    

print(df.dtypes)


    

    




A     int64
B     int64
C     int64
D    object
E      bool
dtype: object

In [8]:

    

df_corr = df.corr()
print(df_corr)


    

    




          A         B         C         E
A  1.000000  0.958927  0.905882 -0.288675
B  0.958927  1.000000  0.987130 -0.346023
C  0.905882  0.987130  1.000000 -0.424522
E -0.288675 -0.346023 -0.424522  1.000000

In [9]:

    

df_nan = df.copy()
df_nan.iloc[[2, 3, 4], 1] = np.nan
print(df_nan)


    

    




   A    B   C  D      E
0  0  0.0   0  a   True
1  1  1.0   1  b  False
2  2  NaN   8  c   True
3  3  NaN  27  d   True
4  4  NaN  64  e  False

In [10]:

    

df_nan_corr = df_nan.corr()
print(df_nan_corr)


    

    




          A    B         C         E
A  1.000000  1.0  0.905882 -0.288675
B  1.000000  1.0  1.000000 -1.000000
C  0.905882  1.0  1.000000 -0.424522
E -0.288675 -1.0 -0.424522  1.000000

In [11]:

    

sns.heatmap(df_corr, vmax=1, vmin=-1, center=0)
plt.savefig('data/dst/seaborn_heatmap_corr_example.png')


    

In [12]:

    

df_house = pd.read_csv('data/src/house_prices_train.csv', index_col=0)


    

In [13]:

    

print(df_house.shape)


    

    




(1460, 80)

In [14]:

    

print(df_house.dtypes.value_counts())


    

    




object     43
int64      34
float64     3
dtype: int64

In [15]:

    

df_house_corr = df_house.corr()


    

In [16]:

    

print(df_house_corr.shape)


    

    




(37, 37)

In [17]:

    

fig, ax = plt.subplots(figsize=(12, 9)) 
sns.heatmap(df_house_corr, square=True, vmax=1, vmin=-1, center=0)
plt.savefig('data/dst/seaborn_heatmap_house_price.png')