用数据框 Python pandas 中的空白替换 0
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Replace 0 with blank in dataframe Python pandas
提问by Joe_ft
I made the following code that takes out all of the zero's from my df. However when there is a number containing a zero it takes them out as well.
我制作了以下代码，从我的 df 中取出所有零。然而，当有一个包含零的数字时，它也会将它们取出。
e.g.
3016.2 316.2
0.235 .235
data_usage_df['Data Volume (MB)'] = data_usage_df['Data Volume (MB)'].str.replace('0', '')
Could you help me to figure out how I do an exact match of the cell that equals 0 and replace it with a blank value.
你能帮我弄清楚我如何对等于 0 的单元格进行精确匹配并将其替换为空白值。
采纳答案by b2002
data_usage_df = data_usage_df.astype(str)
data_usage_df['Data Volume (MB)'].replace(['0', '0.0'], '', inplace=True)
回答by jezrael
I think you need add ^for matching start of string and $for end of string:
我认为您需要添加^以匹配字符串的开头和字符串$的结尾：
data_usage_df['Data Volume (MB)']=data_usage_df['Data Volume (MB)'].str.replace('^0.0$', '')
Sample:
样本：
data_usage_df = pd.DataFrame({'Data Volume (MB)':[3016.2, 0.235, 1.4001, 0, 4.00]})
print (data_usage_df)
runfile('C:/Dropbox/work-joy/so/_t/test.py', wdir='C:/Dropbox/work-joy/so/_t')
Data Volume (MB)
0 3016.2000
1 0.2350
2 1.4001
3 0.0000
4 4.0000
data_usage_df['Data Volume (MB)'] = data_usage_df['Data Volume (MB)'].astype(str)
data_usage_df['Data Volume (MB)']=data_usage_df['Data Volume (MB)'].str.replace('^0.0$', '')
print (data_usage_df)
Data Volume (MB)
0 3016.2
1 0.235
2 1.4001
3
4 4.0
Another solution is converting column to_numericand where is 0give empty space:
另一种解决方案是转换列to_numeric，其中0给出空白空间：
data_usage_df['Data Volume (MB)'] = data_usage_df['Data Volume (MB)'].astype(str)
data_usage_df.ix[pd.to_numeric(data_usage_df['Data Volume (MB)'], errors='coerce') == 0,
['Data Volume (MB)']] = ''
print (data_usage_df)
Data Volume (MB)
0 3016.2
1 0.235
2 1.4001
3
4 4.0
