DataFrame的行和列：df[‘行’, ‘列’]
DataFrame行和列的获取分三个维度
- 行和列选取：df[]，一次只能选取行或列
- 区域选取：df.loc[], df.iloc[], df.ix[]，可以同时为行或列设置筛选条件
- 单元格选取：df.at[], df.iat[]，准确选取某个单元格
先随机生成一个dataframe
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10,5), index=list('abcdefghij'), columns=list('ABCDE'))
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
c -0.359802 -2.049489 -0.615742 -1.953994 0.685243
d 0.232557 1.768284 -0.447015 2.373358 1.220536
e -0.997380 -0.447236 0.632368 -0.352590 -0.064736
f -1.220178 -0.314304 1.202184 0.018326 1.072153
g -1.508916 0.380466 0.359506 -0.742657 -0.373764
h 1.031420 -3.236676 0.444769 1.396802 -0.405590
i 0.166133 -0.051614 -0.146943 0.609431 -0.351814
j 1.857521 -0.159101 0.899745 1.108722 -0.615379
1.1 根据索引获取行
- 获取前3行数据
df[:3]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
c -0.359802 -2.049489 -0.615742 -1.953994 0.685243
- 获取第2到3行数据
df[1:3]
df['b':'c']
A B C D E
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
c -0.359802 -2.049489 -0.615742 -1.953994 0.685243
- 获取特定行数据
df[[True,False,True,False,False,False, True, True, False, True]]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
c -0.359802 -2.049489 -0.615742 -1.953994 0.685243
g -1.508916 0.380466 0.359506 -0.742657 -0.373764
h 1.031420 -3.236676 0.444769 1.396802 -0.405590
j 1.857521 -0.159101 0.899745 1.108722 -0.615379
1.2 根据条件获取行
- 获取A列大于0的行
df[df.A > 0]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
d 0.232557 1.768284 -0.447015 2.373358 1.220536
h 1.031420 -3.236676 0.444769 1.396802 -0.405590
i 0.166133 -0.051614 -0.146943 0.609431 -0.351814
j 1.857521 -0.159101 0.899745 1.108722 -0.615379
- 获取A列和B列大于0的行
df[(df.A > 0) & (df.B > 0)]
A B C D E
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
d 0.232557 1.768284 -0.447015 2.373358 1.220536
- 获取A列或列大于0的行
df[(df.A > 0) | (df.B > 0)]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
d 0.232557 1.768284 -0.447015 2.373358 1.220536
g -1.508916 0.380466 0.359506 -0.742657 -0.373764
h 1.031420 -3.236676 0.444769 1.396802 -0.405590
i 0.166133 -0.051614 -0.146943 0.609431 -0.351814
j 1.857521 -0.159101 0.899745 1.108722 -0.615379
1.3 获取列
df['A']
df[['A']]
df[['A', 'B']]
df[df.columns[0:2]]
- df.loc[] 只能使用标签索引，不能使用整数索引，通过便签索引切边进行筛选时，前闭后闭。
- df.iloc[] 只能使用整数索引，不能使用标签索引，通过整数索引切边进行筛选时，前闭后开。
- df.ix[]既可以使用标签索引，也可以使用整数索引。
2.1 df.loc[]
- 获取a行
df.loc['a']
df.loc['a', :]
A 0.299206
B -0.383297
C -0.931467
D -0.591609
E -1.131105
Name: a, dtype: float64
df.loc[['a']]
df.loc[['a'], :]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
- 获取a, b, d行
df.loc[['a', 'b', 'd']]
df.loc[['a', 'b', 'd'], :]
df[[True, True, False, True, False, False, False, True, False, True]]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
d 0.232557 1.768284 -0.447015 2.373358 1.220536
- 获取a到d行
df.loc['a':'d', :]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
c -0.359802 -2.049489 -0.615742 -1.953994 0.685243
d 0.232557 1.768284 -0.447015 2.373358 1.220536
- 选取A列大于0的行
df.loc[df.A > 0]
df.loc[df.A > 0, :]
A B C D E
a 0.299206 -0.383297 -0.931467 -0.591609 -1.131105
b 0.074351 0.791849 1.637467 -1.408712 -1.376527
d 0.232557 1.768284 -0.447015 2.373358 1.220536
h 1.031420 -3.236676 0.444769 1.396802 -0.405590
i 0.166133 -0.051614 -0.146943 0.609431 -0.351814
j 1.857521 -0.159101 0.899745 1.108722 -0.615379
df.loc[:, 'A']
df.loc[:, ['A', 'C']]
df.loc[:, 'A':'C']
df.loc['c', 'B']
df.loc[((df.A > 0) & (df.B > 0)), ['C', 'D']]
df.loc['a', :] = 10
df.loc[:, 'B'] = 50
df.loc[['b', 'c'], 'C':'F'] = 30
df.loc[df.C < 0] = 0
Example
tuples = [
('cobra', 'mark i'), ('cobra', 'mark ii'),
('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
('viper', 'mark ii'), ('viper', 'mark iii')
]
index = pd.MultiIndex.from_tuples(tuples)
values = [[12, 2], [0, 4], [10, 20],
[1, 4], [7, 1], [16, 36]]
df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
df
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
df.loc['cobra']
max_speed shield
mark i 12 2
mark ii 0 4
df.loc[('cobra', 'mark ii')]
max_speed 0
shield 4
Name: (cobra, mark ii), dtype: int64
df.loc[[('cobra', 'mark ii')]]
max_speed shield
cobra mark ii 0 4
df.loc['cobra', 'mark i']
max_speed 12
shield 2
Name: (cobra, mark i), dtype: int64
df.loc[('cobra', 'mark i'), 'shield']
2
df.loc[('cobra', 'mark i'):'viper']
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
mark iii 16 36
df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
max_speed shield
cobra mark i 12 2
mark ii 0 4
sidewinder mark i 10 20
mark ii 1 4
viper mark ii 7 1
2.2 df.iloc[ ]
- 选取第二行
df.iloc[1]
df.iloc[1, :]
df.iloc[[1]]
df.iloc[[1], :]
- 选取前三行
df.iloc[:3, :]
df.iloc[:3]
- 选取第一、三、五行
df.iloc[[1, 3, 5]]
df.iloc[[1, 3, 5], :]
- 选取第二列
df.iloc[:, 1]
- 选取前三列
df.iloc[:, 0:3]
df.iloc[:,:3]
- 选取第一三四列
df.iloc[:, [0, 2, 3]]
- 选取第一行第二列的值
df.iloc[0, 1]
- 选取第二三行的第二到四列
df.iloc[[1,2], 1:4]
2.3 df.ix[ ]
可以混合标签索引和整数索引
However, when an axis is integer based, ONLY label based access and not positional access is supported. Thus, in such cases, it’s usually better to be explicit and use .iloc or .loc.
- df.at[ ] 只能使用 标签索引
- df.iat[ ] 只能使用 *整数索引
3.1 df.at[]
- 获取c行C列的值
df.at['c', 'C']
- 把c行C列赋值为10
df.at['c', 'C'] = 10
3.2 df.iat[]
- 获取第三行第三列的值
df.iat[2, 2]
- 把第三行第三列赋值为10
df.iat[2, 2] = 10
Original: https://blog.csdn.net/weixin_46599926/article/details/122795057
Author: 羊羊猪
Title: pandas行和列的获取
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