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.
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.
data_usage_df = data_usage_df.astype(str)
data_usage_df['Data Volume (MB)'].replace(['0', '0.0'], '', inplace=True)
str remove the quotes from around the 0 and 0.0.I think you need add ^ for matching start of string and $ for end of string:
data_usage_df['Data Volume (MB)']=data_usage_df['Data Volume (MB)'].str.replace('^0.0$', '')
Sample:
data_usage_df = pd.DataFrame({'Data Volume (MB)':[3016.2, 0.235, 1.4001, 0, 4.00]})
print (data_usage_df)
runfile('C:/Dropbox/work-joy/so/_t/test.py', wdir='C:/Dropbox/work-joy/so/_t')
Data Volume (MB)
0 3016.2000
1 0.2350
2 1.4001
3 0.0000
4 4.0000
data_usage_df['Data Volume (MB)'] = data_usage_df['Data Volume (MB)'].astype(str)
data_usage_df['Data Volume (MB)']=data_usage_df['Data Volume (MB)'].str.replace('^0.0$', '')
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_numeric and where is 0 give empty space:
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
