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

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

filled_DF = DF_obj.fillna(0)
filled_DF

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

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

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

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

DF_no_NaN = DF_obj.dropna(axis=1)
DF_no_NaN

Python for Data Science - Treating missing values

原文：https://www.cnblogs.com/keepmoving1113/p/14222788.html