Stop Pandas from converting int to float
If you set dtype=object, your series will be able to contain arbitrary data types:
df["int"] = pd.Series([], dtype=object)df["str"] = pd.Series([], dtype=str)df.loc[0] = [0, "zero"]print(df)print()df.loc[1] = [1, None]print(df) int str0 0 zero1 NaN NaN int str0 0 zero1 1 None
As of pandas 1.0.0 I believe you have another option, which is to first use convert_dtypes. This converts the dataframe columns to dtypes that support pd.NA, avoiding the issues with NaN/None.
...df = df.convert_dtypes()df.loc[1] = [1, None]print(df)# int str# 0 0 zero# 1 1 NaN
If you use DataFrame.append to add the data, the dtypes are preserved, and you do not have to recast or rely on object:
In [157]: dfOut[157]: int str0 0 zeroIn [159]: df.append(pd.DataFrame([[1, None]], columns=['int', 'str']), ignore_index=True)Out[159]: int str0 0 zero1 1 None