Dump (unique) values to CSV / to_csv in Pandas
This post will show you how to use to_csv in Pandas and avoid errors like:
AttributeError: 'numpy.ndarray' object has no attribute 'to_csv'
For example if you like to write unique values from Pandas to a CSV file by method to_csv from this data:
| Date | Time | Latitude | Longitude | Depth | Magnitude Type |
|---|---|---|---|---|---|
| 01/02/1965 | 13:44:18 | 19.246 | 145.616 | 131.6 | MW |
| 01/04/1965 | 11:29:49 | 1.863 | 127.352 | 80.0 | MW |
| 01/05/1965 | 18:05:58 | -20.579 | -173.972 | 20.0 | MW |
| 01/08/1965 | 18:49:43 | -59.076 | -23.557 | 15.0 | MW |
| 01/09/1965 | 13:32:50 | 11.938 | 126.427 | 15.0 | MW |
by:
df['Magnitude Type'].unique().to_csv()
This will result into:
AttributeError: 'numpy.ndarray' object has no attribute 'to_csv'
The reason for the error is that method unique returns an array and not pd.Series or pd.DataFrame object:
array(['MW', 'ML', 'MH', 'MS', 'MB', 'MWC', 'MD', nan, 'MWB', 'MWW',
'MWR'], dtype=object)
For Pandas you have two different option depending on your context.
Series
Convert the result of method unique to a Pandas Series by pd.Series:
pd.Series(df['Magnitude Type'].unique()).to_csv('data.csv')
DataFrame
If the pd.Series it's not suitable or doesn't work for your case then you can use pd.DataFrame:
pd.DataFrame(df['Magnitude Type'].unique()).to_csv('data.csv')
Skip headers and index
Finally if you like to exclude the headers and the index from the output CSV file you can use arguments: header=None, index=None
pd.DataFrame(df['Magnitude Type'].unique()).to_csv('data.csv', header=None, index=None)