pandas DataFrame的常用方法

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____tz_zs

参数 axis

axis=0 对每一列进行操作

axis=1 对每一行进行操作

参数 skipna

计算中，pandas 会默认排除NaN值，设置 skipna=False 将不再排除 NaN 值

.

#!/usr/bin/python2.7
# -*- coding:utf-8 -*-

"""
@author:    tz_zs
"""

import numpy as np
import pandas as pd


data = [[1, 2, np.nan], [2, np.nan, 3], [7, 8, 9], [3, 4, 5]]
date_range = pd.date_range(start="20180701", periods=4)
df = pd.DataFrame(data=data, index=date_range,
                   columns=['a', 'b', 'c'])
print df
"""
            a    b    c
2018-07-01  1  2.0  NaN
2018-07-02  2  NaN  3.0
2018-07-03  7  8.0  9.0
2018-07-04  3  4.0  5.0
"""

.

sum

求和 df.sum()

# 对每一列求和,默认排除NaN值
print df.sum()
"""
a    13.0
b    14.0
c    17.0
dtype: float64
"""

# 对每一行求和,默认排除NaN值
print df.sum(axis=1)
"""
2018-07-01     3.0
2018-07-02     5.0
2018-07-03    24.0
2018-07-04    12.0
Freq: D, dtype: float64
"""


# 设置 skipna=False 将不再排除 NaN 值
print df.sum(skipna=False)
"""
a    13.0
b     NaN
c     NaN
dtype: float64
"""

print df.sum(axis=1,skipna=False)
"""
2018-07-01     NaN
2018-07-02     NaN
2018-07-03    24.0
2018-07-04    12.0
Freq: D, dtype: float64
"""

.

mean

求平均 df.mean

# 对每一行求平均值
print df.mean(axis=1)
"""
2018-07-01    1.5
2018-07-02    2.5
2018-07-03    8.0
2018-07-04    4.0
Freq: D, dtype: float64
"""


print df.mean(axis=1, skipna=False)
"""
2018-07-01    NaN
2018-07-02    NaN
2018-07-03    8.0
2018-07-04    4.0
Freq: D, dtype: float64
"""

.

max、min

最大最小值 df.max、df.min

# 最大最小值
print df.max()
print df.min()
"""
a    7.0
b    8.0
c    9.0
dtype: float64
a    1.0
b    2.0
c    3.0
dtype: float64
"""

.

idxmax、idxmin

最大最小值的索引 df.idxmax、df.idxmin

# 返回每一列中最大值的索引
print df.idxmax()
# 返回每一列中最小值的索引
print df.idxmin()
"""
a   2018-07-03
b   2018-07-03
c   2018-07-03
dtype: datetime64[ns]
a   2018-07-01
b   2018-07-01
c   2018-07-02
dtype: datetime64[ns]
"""

.

cumsum

累加 df.cumsum

# 对每一列累加
print df.cumsum()
"""
               a     b     c
2018-07-01   1.0   2.0   NaN
2018-07-02   3.0   NaN   3.0
2018-07-03  10.0  10.0  12.0
2018-07-04  13.0  14.0  17.0
"""

.

print df.median(axis=1)  # 中位数
"""
2018-07-01    1.5
2018-07-02    2.5
2018-07-03    8.0
2018-07-04    4.0
Freq: D, dtype: float64
"""

.

mad

根据平均值计算平均绝对离差 df.mad

print df.mad()  # 根据平均值计算平均绝对离差
"""
a    1.875000
b    2.222222
c    2.222222
dtype: float64
"""

.

std

标准差 df.std

print df.std()  # 标准差
"""
a    2.629956
b    3.055050
c    3.055050
dtype: float64
"""

.

var

方差 df.var

print df.var()  # 方差
"""
a    6.916667
b    9.333333
c    9.333333
dtype: float64
"""

.

diff

一阶差分 df.diff

print df.diff()  # 计算一阶差分
"""
              a    b    c
2018-07-01  NaN  NaN  NaN
2018-07-02  1.0  NaN  NaN
2018-07-03  5.0  NaN  6.0
2018-07-04 -4.0 -4.0 -4.0
"""

.

pct_change

百分数变化 df.pct_change

pandas.Series.pct_change 、 pandas.DataFrame.pct_change

print df.pct_change()  # 计算百分数变化(在列上计算)
"""
                   a    b         c
2018-07-01       NaN  NaN       NaN
2018-07-02  1.000000  NaN       NaN
2018-07-03  2.500000  3.0  2.000000
2018-07-04 -0.571429 -0.5 -0.444444
"""
print df.pct_change(axis=1)  # 计算百分数变化(在行上计算)
"""
             a         b      c
2018-07-01 NaN  1.000000    NaN
2018-07-02 NaN       NaN  0.500
2018-07-03 NaN  0.142857  0.125
2018-07-04 NaN  0.333333  0.250
"""

.

corr

pandas.DataFrame.corr

计算列与列之间的相关性，不计算包括NA / null值的列。

DataFrame.corr(method='pearson', min_periods=1)

参数：

method：

• pearson：皮尔逊相关系数
• kendall：肯德尔等级相关系数
• spearman：斯皮尔曼等级相关系数

min_periods：为获取有效结构，每对列所需的最小观察数据量

返回：
关于原始DataFrame列与列之间相关性的DataFrame对象。

.

#!/usr/bin/python2.7
# -*- coding:utf-8 -*-

"""
@author:    tz_zs
"""

import pandas as pd

list_l = [[1, 3, 3, 5, ], [11, 7, 15, 13], [4, 2, 7, 9]]
index = ["2018-07-02", "2018-07-03", "2018-07-04"]
col = ['a', 'b', 'c', 'd']
df = pd.DataFrame(list_l, index=index, columns=col)
print(df)
"""
             a  b   c   d
2018-07-02   1  3   3   5
2018-07-03  11  7  15  13
2018-07-04   4  2   7   9
"""

df_corr = df.corr()
print(df_corr)
print(type(df_corr))
"""
          a         b         c         d
a  1.000000  0.883852  0.999322  0.974355
b  0.883852  1.000000  0.866025  0.755929
c  0.999322  0.866025  1.000000  0.981981
d  0.974355  0.755929  0.981981  1.000000
<class 'pandas.core.frame.DataFrame'>
"""

.

#!/usr/bin/python2.7
# -*- coding:utf-8 -*-

"""
@author:    tz_zs
"""

import pandas as pd

list_l = [[1, 3, 3, 5, ], [11, 7, 15, 13], [4, 2, 7, None]]
index = ["2018-07-02", "2018-07-03", "2018-07-04"]
col = ['a', 'b', 'c', 'd']
df = pd.DataFrame(list_l, index=index, columns=col)
print(df)
"""
             a  b   c     d
2018-07-02   1  3   3   5.0
2018-07-03  11  7  15  13.0
2018-07-04   4  2   7   NaN
"""

df_corr = df.corr()
print(df_corr)
print(type(df_corr))
"""
          a         b         c    d
a  1.000000  0.883852  0.999322  1.0
b  0.883852  1.000000  0.866025  1.0
c  0.999322  0.866025  1.000000  1.0
d  1.000000  1.000000  1.000000  1.0
<class 'pandas.core.frame.DataFrame'>
"""

.

end