I have a Pandas DataFrame with a column with TimeStamps. I can select date ranges from this column. But after I make change to other columns in the DataFrame, I can no longer and I get the error “TypeError: ‘>’ not supported between instances of ‘int’ and ‘str'”.
The code below reproduce the problem:
- Generate a DataFrame with some random numbers
- Add a column with dates
Select on the date column
df = pd.DataFrame(np.random.random((200,3))) df['date'] = pd.date_range('2000-1-1', periods=200, freq='D') mask = (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10') print(df.loc[mask])
All good:
0 1 2 date 153 0.280575 0.810817 0.534509 2000-06-02 154 0.490319 0.873906 0.465698 2000-06-03 155 0.070790 0.898340 0.390777 2000-06-04 156 0.896007 0.824134 0.134484 2000-06-05 157 0.539633 0.814883 0.976257 2000-06-06 158 0.772454 0.420732 0.499719 2000-06-07 159 0.498020 0.495946 0.546043 2000-06-08 160 0.562385 0.460190 0.480170 2000-06-09 161 0.924412 0.611929 0.459360 2000-06-10
However, now I set column 0 to 0 if it exceeds 0.7 and repeat:
df[df[0] > 0.7] = 0 mask = (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10')
This gives the error:
TypeError: '>' not supported between instances of 'int' and 'str'
Why does this happen and how do I avoid it?
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Answer
You can compare a timestamp (Timestamp('2000-01-01 00:00:00')) to a string, pandas will convert the string to Timestamp for you. But once you set the value to 0, you cannot compare an int to a str.
Another way to go around this is to change order of your operations.
filters = df[0] > 0.7 mask = (df['date'] > '2000-6-1') & (df['date'] <= '2000-6-10') df[filters] = 0 print(df.loc[mask & filters])
Also, you mentioned you want to set column 0 to 0 if it exceeds 0.7, so df[df[0]>0.7] = 0 does not do exactly what you want: it sets the entire rows to 0. Instead:
df.loc[df[0] > 0.7, 0] = 0
Then you should not have any problem with the original mask.