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When working with dataframes, it is often necessary to adjust the content of individual cells. In this case, the task is to insert a specific list, 'abc', into cell 1B in a dataframe. Various attempts have been made, but each has encountered its own challenges.
Initially, an attempt to assign the entire list directly to the cell using df.ix[1,'B'] = abc resulted in a ValueError due to a mismatch in the number of keys and values. Using df.ix[1,'B'] = [abc] instead creates a list with the 'abc' list as its sole element, which is not desired.
Converting the list to a string using df.ix[1,'B'] = ', '.join(abc) produces the intended content as a string rather than a list. Similarly, using df.ix[1,'B'] = [', '.join(abc)] creates a list with a single string element.
Subsequent attempts to insert the list into dataframes with more complex column types, involving both integers and strings, encounter an error. This suggests that the issue lies in the mixed data types of the columns.
To resolve this, it is recommended to use the at attribute instead of loc. This method consistently refers to a single value, avoiding the issues encountered with loc. Additionally, ensuring that the column being inserted into has dtype=object prevents further errors.
For instance, the following code successfully inserts the 'abc' list into cell 1B of the df2 dataframe:
import pandas as pd abc = ['foo', 'bar'] df2 = pd.DataFrame(data={'A': [12, 23], 'B': [None, None], 'C': ['bla', 'bla bla']}) df2['B'] = df2['B'].astype('object') # Ensure 'B' has dtype=object df2.at[1, 'B'] = abc print(df2)
Output:
A B C 0 12 NaN bla 1 23 [foo, bar] bla bla
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