1. Not specifying correct dataframe column names or indices:
When creating a dataframe, if you specify column names that do not match the keys in the data dictionary, the resulting dataframe will have missing or NaN values for those columns. In the example’s code incorrect approach, a column named ‘Gender’ is specified that does not exist in the dictionary.
Ensure that the column names provided in the columns parameter match the keys in the data dictionary.
2. Not resetting the index after modifying the dataframe:
After performing operations that modify the structure of a dataframe, such as dropping rows or columns, the indices may not be consecutive anymore. In this example, the code mistakenly tries to access a row with index 0 after dropping that row, resulting in a KeyError.
In order to resolve this, reset the index of the dataframe using the reset_index function after performing operations that modify the dataframe structure.
3. Not performing operations on the correct axis:
When performing operations on a dataframe, it is important to specify the correct axis along which the operation should be performed. In the incorrect approach of this example, the code computes the sum of values by row (axis=1), but the dataframe structure does not support such an operation, resulting in a ValueError.
In order to resolve this, specify the correct axis when performing operations on dataframes. To compute the sum of values by column, use axis=0.
4. Not using inplace parameter correctly when modifying a dataframe:
When modifying a dataframe, it is important to use the inplace parameter correctly to ensure that the modifications are applied to the original dataframe. In this example, the incorrect approach involves dropping the row without using inplace=True, resulting in no changes to the original dataframe.
Use the inplace=True parameter when modifying a dataframe to ensure that the modifications are applied to the original dataframe.
These are a few common mistakes when using dataframes in Python with the pandas library. By understanding these mistakes and applying the appropriate solutions, you can work with dataframes more effectively and avoid potential errors.