df_SPI数据类型为int64类型，首先需将其转为字符串
df_SPI['Date'] = df_SPI['Date'].astype('str')
index4 = df_SPI.index[df_SPI["Date"].str.len() == 7]
df_SPI.loc[index4,["Date"]] = '0' + df_SPI.loc[index4,["Date"]]
df_SPI数据类型为int64类型，首先需将其转为字符串#'Date'所在列数据转为字符串df_SPI['Date'] = df_SPI['Date'].astype('str')#取得‘Date’中字符串大小为7的行的indexindex4 = df_SPI.index[df_SPI["Date"].str.len() == 7]#将‘Date’列中符合条件的index所在行前面加上字符串'0'df_SPI.loc[index4,["Date"]] = '0' + df_SPI.loc[in
df.loc[df['col1'] !=' pre','col2']=Nonpre
以上这篇python pandas 如何替换某列的一个值就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持软件开发网。
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pandas在某行某列中加字符串，筛选某行某列含字符串
import pandas as pd
chengji=[[100,95,100,98],[90,98,99,94],[88,95,98,95],[99,98,97,92],[95,90,96,88],[94,94,93,77]]
data=pd.DataFrame(chengji,columns=['语文','类别','数学','政治'])...
>>> import numpy as np
>>> ts1 = [0, 1, np.nan, np.nan, np.nan, np.nan]
>>> ts2 = [0, 2, np.nan, 3, np.nan, np.nan]
>>> d = {'X': ts1, 'Y': ts2, 'Z': ts2}
>>> df = pd.DataFrame(data=d)
X Y Z
0 0.0 0.0 0.0
1 1.0 2.0 2.0
2 NaN NaN NaN
3 NaN 3.0 3.0
4 NaN Na
>>> import numpy as np
>>> import pandas as pd
Backend TkAgg is interactive backend. Turning interactive mode on.
>>> np.random.seed(1)
>>> df_test = pd.DataFrame(np.random.randn(4,4)* 4 + 3)
>>> df_test
0 1 2 3
0 9.497381 0.552974 0.887313 -1.291874
1 6.461631 -6.206155 9.979247 -0.0
# 现在可以使用strftime函数来将时间列格式化为字符串
df['time_column'] = df['time_column'].dt.strftime('%Y-%m-%d %H:%M:%S')
上面的代码将时间列格式化为'YYYY-MM-DD HH:MM:SS'的字符串格式。关于strftime函数的更多信息，可以参考这篇文章：https://www.w3schools.com/python/ref_string_strftime.asp 。
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LV0628:
