pandas的基本用法(四)——处理缺失数据

文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

本文主要是关于pandas的一些基本用法。

#!/usr/bin/env python
# _*_ coding: utf-8 _*_

import pandas as pd
import numpy as np


# Test 1
# 定义数据
dates = pd.date_range('20170101', periods = 6)
df = pd.DataFrame(np.arange(24).reshape((6, 4)), index = dates, columns = ['A', 'B', 'C', 'D'])

# 假设缺少数据
df.iloc[1, 1] = np.nan
df.iloc[2, 2] = np.nan
print df

# Test 1 result
             A     B     C   D
2017-01-01   0   1.0   2.0   3
2017-01-02   4   NaN   6.0   7
2017-01-03   8   9.0   NaN  11
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23

# Test 2
# 按行或列来舍弃数据, how = any or all, any是默认值
print df.dropna(axis = 0, how = 'any')

# 填充数据
print df.fillna(value = 0)

# 判断是否缺失数据
print df.isnull()

# 判断是否存在缺失数据的情况
print np.any(df.isnull() == True)

# Test 2 result
             A     B     C   D
2017-01-01   0   1.0   2.0   3
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23

             A     B     C   D
2017-01-01   0   1.0   2.0   3
2017-01-02   4   0.0   6.0   7
2017-01-03   8   9.0   0.0  11
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23

                A      B      C      D
2017-01-01  False  False  False  False
2017-01-02  False   True  False  False
2017-01-03  False  False   True  False
2017-01-04  False  False  False  False
2017-01-05  False  False  False  False
2017-01-06  False  False  False  False

True