Timeline for How to deal with errors of defining data types in pandas' read_csv ()?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Apr 28, 2020 at 10:59 | comment | added | Liliana | Thanks. Good idea to compare input and output files. | |
| Apr 23, 2020 at 11:08 | comment | added | Bruno Lubascher |
@Liliana just be careful that you have errors='coerce' which could mean that you lose some data unwillingly.
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| Apr 23, 2020 at 11:00 | comment | added | Liliana | Thanks for the new function. I solved this problem by converting columns datatype after importing them into df. I passed a list of columns I needed to convert into for loop. That worked. For example: col_list=['col1', 'col2', 'col5', etc...] and than using for loop for col in col_list: df[col]=pd.to_numeric(df[col], errors='coerce') Thanks | |
| Apr 21, 2020 at 14:35 | history | answered | Bruno Lubascher | CC BY-SA 4.0 |