Python for Data Science - Treating missing values

Chapter 2 - Data Preparation Basics

Segment 2 - Treating missing values

import numpy as np
import pandas as pd 

from pandas import Series, DataFrame

Figuring out what data is missing

missing = np.nan

series_obj = Series(['row 1','row 2',missing,'row 4','row 5','row 6',missing,'row 8'])
series_obj

0    row 1
1    row 2
2      NaN
3    row 4
4    row 5
5    row 6
6      NaN
7    row 8
dtype: object

series_obj.isnull()

0    False
1    False
2     True
3    False
4    False
5    False
6     True
7    False
dtype: bool

Filling in for missing values

np.random.seed(25)
DF_obj = DataFrame(np.random.rand(36).reshape(6,6))
DF_obj

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	0.113041
2	0.447031	0.585445	0.161985	0.520719	0.326051	0.699186
3	0.366395	0.836375	0.481343	0.516502	0.383048	0.997541
4	0.514244	0.559053	0.034450	0.719930	0.421004	0.436935
5	0.281701	0.900274	0.669612	0.456069	0.289804	0.525819

DF_obj.loc[3:5, 0] = missing
DF_obj.loc[1:4, 5] = missing
DF_obj

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	NaN
2	0.447031	0.585445	0.161985	0.520719	0.326051	NaN
3	NaN	0.836375	0.481343	0.516502	0.383048	NaN
4	NaN	0.559053	0.034450	0.719930	0.421004	NaN
5	NaN	0.900274	0.669612	0.456069	0.289804	0.525819

filled_DF = DF_obj.fillna(0)
filled_DF

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	0.000000
2	0.447031	0.585445	0.161985	0.520719	0.326051	0.000000
3	0.000000	0.836375	0.481343	0.516502	0.383048	0.000000
4	0.000000	0.559053	0.034450	0.719930	0.421004	0.000000
5	0.000000	0.900274	0.669612	0.456069	0.289804	0.525819

filled_DF = DF_obj.fillna({0:0.1, 5:1.25})
filled_DF

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	1.250000
2	0.447031	0.585445	0.161985	0.520719	0.326051	1.250000
3	0.100000	0.836375	0.481343	0.516502	0.383048	1.250000
4	0.100000	0.559053	0.034450	0.719930	0.421004	1.250000
5	0.100000	0.900274	0.669612	0.456069	0.289804	0.525819

fill_DF = DF_obj.fillna(method='ffill')
fill_DF

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	0.117376
2	0.447031	0.585445	0.161985	0.520719	0.326051	0.117376
3	0.447031	0.836375	0.481343	0.516502	0.383048	0.117376
4	0.447031	0.559053	0.034450	0.719930	0.421004	0.117376
5	0.447031	0.900274	0.669612	0.456069	0.289804	0.525819

Counting missing values

np.random.seed(25)
DF_obj = DataFrame(np.random.rand(36).reshape(6,6))
DF_obj.loc[3:5, 0] = missing
DF_obj.loc[1:4, 5] = missing
DF_obj

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
1	0.684969	0.437611	0.556229	0.367080	0.402366	NaN
2	0.447031	0.585445	0.161985	0.520719	0.326051	NaN
3	NaN	0.836375	0.481343	0.516502	0.383048	NaN
4	NaN	0.559053	0.034450	0.719930	0.421004	NaN
5	NaN	0.900274	0.669612	0.456069	0.289804	0.525819

DF_obj.isnull().sum()

0    3
1    0
2    0
3    0
4    0
5    4
dtype: int64

Filtering out missing values

DF_no_NaN = DF_obj.dropna()
DF_no_NaN

	0	1	2	3	4	5
0	0.870124	0.582277	0.278839	0.185911	0.4111	0.117376

DF_no_NaN = DF_obj.dropna(axis=1)
DF_no_NaN

	1	2	3	4
0	0.582277	0.278839	0.185911	0.411100
1	0.437611	0.556229	0.367080	0.402366
2	0.585445	0.161985	0.520719	0.326051
3	0.836375	0.481343	0.516502	0.383048
4	0.559053	0.034450	0.719930	0.421004
5	0.900274	0.669612	0.456069	0.289804