Pandas-数据操作-字符串型（一）：常用方法【str（自动过滤NaN值）、索引】

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Pandas针对字符串配备的一套方法，使其易于对数组的每个元素进行操作。

一、str：通过str访问，且自动排除丢失/ NA值

通过str访问，且自动排除丢失/ NA值

直接通过.str调用字符串方法
可以对Series、Dataframe使用
自动过滤NaN值

import numpy as np
import pandas as pd# 通过str访问，且自动排除丢失/ NA值
# 直接通过.str调用字符串方法
# 可以对Series、Dataframe使用
# 自动过滤NaN值s = pd.Series(['A', 'b', 'C', 'bbhello', '123', np.nan, 'hj'])
df = pd.DataFrame({'key1': list('abcdef'),'key2': ['hee', 'fv', 'w', 'hija', '123', np.nan]})
print("s = \n", s)
print('-' * 50)
print("df = \n", df)
print('-' * 200)print("s.str.count('b') = \n", s.str.count('b'))
print('-' * 50)
print("df['key2'].str.upper() = \n", df['key2'].str.upper())
print('-' * 200)# df.columns是一个Index对象，也可使用.str
df.columns = df.columns.str.upper()
print("df = \n", df)
print('-' * 200)

打印结果：

s = 
0          A
1          b
2          C
3    bbhello
4        123
5        NaN
6         hj
dtype: object
--------------------------------------------------
df = key1  key2
0    a   hee
1    b    fv
2    c     w
3    d  hija
4    e   123
5    f   NaN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
s.str.count('b') = 0    0.0
1    1.0
2    0.0
3    2.0
4    0.0
5    NaN
6    0.0
dtype: float64
--------------------------------------------------
df['key2'].str.upper() = 0     HEE
1      FV
2       W
3    HIJA
4     123
5     NaN
Name: key2, dtype: object
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
df = KEY1  KEY2
0    a   hee
1    b    fv
2    c     w
3    d  hija
4    e   123
5    f   NaN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Process finished with exit code 0

二、字符串索引

import numpy as np
import pandas as pd# 字符串索引s = pd.Series(['A', 'b', 'C', 'bbhello', '123', np.nan, 'hj'])
df = pd.DataFrame({'key1': list('abcdef'),'key2': ['hee', 'fv', 'w', 'hija', '123', np.nan]})# 取第一个字符
data1 = s.str[0]
print("取第一个字符: data1 = s.str[0] = \n", data1)
print('-' * 200)
# 取前两个字符
data2 = s.str[:2]
print("取前两个字符: data2 = s.str[:2] = \n", data2)
print('-' * 200)# str之后和字符串本身索引方式相同
data3 = df['key2'].str[:2]
print("data3 = df['key2'].str[:2] = \n", data3)
print('-' * 200)

打印结果：

取第一个字符: data1 = s.str[0] = 0      A
1      b
2      C
3      b
4      1
5    NaN
6      h
dtype: object
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
取前两个字符: data2 = s.str[:2] = 0      A
1      b
2      C
3     bb
4     12
5    NaN
6     hj
dtype: object
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
data3 = df['key2'].str[:2] = 0     he
1     fv
2      w
3     hi
4     12
5    NaN
Name: key2, dtype: object
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Process finished with exit code 0