dataFrame数据处理

如果想要应用自定义的函数，或者把其他库中的函数应用到 Pandas 对象中，有以下三种方法：

• 1) 操作整个 DataFrame 的函数：pipe()
• 2) 操作行或者列的函数：apply()
• 3) 操作单一元素的函数：applymap()

1  pipe

import pandas as pd
import numpy as np
#自定义函数
def adder(ele1,ele2):
   return ele1+ele2
#操作DataFrame
df = pd.DataFrame(np.random.randn(4,3),columns=['c1','c2','c3'])
#相加前
print(df)
#相加后
print(df.pipe(adder,5))

结果：
        c1        c2        c3
0  1.908222 -0.377214 -0.967820
1 -0.386066  0.298205  1.143535
2 -1.469431  0.287798 -1.371165
3 -0.724862  0.821063 -0.625724
         c1        c2        c3
0  6.908222  4.622786  4.032180
1  4.613934  5.298205  6.143535
2  3.530569  5.287798  3.628835
3  4.275138  5.821063  4.374276

2  apply()

 按行处理

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print(df)
print (df.apply(np.mean,axis=1))

0 -0.865303  0.380977 -1.371948
1 -0.735269  0.552093 -0.101040
2  0.329665 -0.710652 -1.206729
3 -0.480575  0.156047  1.099046
4  0.466110  0.295210 -0.419494
0   -0.618758
1   -0.094739
2   -0.529239
3    0.258173
4    0.113942
dtype: float64

默认按列处理

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print(df)
print(df.apply(lambda x: x.max() - x.min()))

结果
     col1      col2      col3
0 -0.909495 -2.009906  1.532436
1 -0.276285  1.607327 -0.394461
2  0.490701  0.185447 -0.320529
3  0.738601  1.384119 -0.215178
4  0.933595  0.217680 -0.545816
col1    1.843090
col2    3.617233
col3    2.078252
dtype: float64

applymap

import pandas as pd
import numpy as np
#自定义函数
df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
print(df.applymap(lambda x:x*10))
print(df.apply(np.mean))

结果
        col1       col2       col3
0   3.826257  -6.184871 -10.162795
1 -20.665111   1.466812   1.266041
2  -3.371027  18.351201  -4.044751
3 -10.797481 -21.777112   1.862200
4   5.660287 -17.092084  11.905230
col1   -0.506941
col2   -0.504721
col3    0.016518
dtype: float64