In this article, we will try to see different ways of removing the Empty column, Null column, and zeros value column. First, We will create a sample data frame and then we will perform our operations in subsequent examples by the end you will get a strong hand knowledge on how to handle this situation with pandas.
Approach:
- Import required python library.
- Create a sample Data Frame.
- Use the Pandas dropna() method, It allows the user to analyze and drop Rows/Columns with Null values in different ways.
- Display updated Data Frame.
Syntax: DataFrameName.dropna(axis=0, how=’any’, inplace=False)
Parameters:
- axis: axis takes int or string value for rows/columns. Input can be 0 or 1 for Integer and ‘index’ or ‘columns’ for String.
- how: how takes string value of two kinds only (‘any’ or ‘all’). ‘any’ drops the row/column if ANY value is Null and ‘all’ drops only if ALL values are null.
- inplace: It is a boolean which makes the changes in the data frame itself if True.
Sample Data:
This is the sample data frame on which we will perform different operations.
Python3
# import required libraries import numpy as np import pandas as pd # create a Dataframe Mydataframe = pd.DataFrame({'FirstName': ['Vipul', 'Ashish', 'Milan'], "Gender": ["", "", ""], "Age": [0, 0, 0]}) Mydataframe['Department'] = np.nan # show the dataframe print(Mydataframe) |
Output:
Example 1:
Remove all null value column.
Python3
# import required libraries import numpy as np import pandas as pd # create a Dataframe Mydataframe = pd.DataFrame({'FirstName': ['Vipul', 'Ashish', 'Milan'], "Gender": ["", "", ""], "Age": [0, 0, 0]}) Mydataframe['Department'] = np.nan display(Mydataframe) Mydataframe.dropna(how='all', axis=1, inplace=True) # show the dataframe display(Mydataframe) |
Output:
Example 2:
Replace all Empty places with null and then Remove all null values column with dropna function.
Python3
# import required libraries import numpy as np import pandas as pd # create a Dataframe Mydataframe = pd.DataFrame({'FirstName': ['Vipul', 'Ashish', 'Milan'], "Gender": ["", "", ""], "Age": [0, 0, 0]}) Mydataframe['Department'] = np.nan display(Mydataframe) nan_value = float("NaN") Mydataframe.replace("", nan_value, inplace=True) Mydataframe.dropna(how='all', axis=1, inplace=True) # show the dataframe display(Mydataframe) |
Output:
Example 3:
Replace all zeros places with null and then Remove all null values column with dropna function.
Python3
# import required libraries import numpy as np import pandas as pd # create a Dataframe Mydataframe = pd.DataFrame({'FirstName': ['Vipul', 'Ashish', 'Milan'], "Gender": ["", "", ""], "Age": [0, 0, 0]}) Mydataframe['Department'] = np.nan display(Mydataframe) nan_value = float("NaN") Mydataframe.replace(0, nan_value, inplace=True) Mydataframe.dropna(how='all', axis=1, inplace=True) # show the dataframe display(Mydataframe) |
Output:
Example 4:
Replace all zeros and empty places with null and then Remove all null values column with dropna function.
Python3
# import required libraries import numpy as np import pandas as pd # create a Dataframe Mydataframe = pd.DataFrame({'FirstName': ['Vipul', 'Ashish', 'Milan'], "Gender": ["", "", ""], "Age": [0, 0, 0]}) Mydataframe['Department'] = np.nan display(Mydataframe) nan_value = float("NaN") Mydataframe.replace(0, nan_value, inplace=True) Mydataframe.replace("", nan_value, inplace=True) Mydataframe.dropna(how='all', axis=1, inplace=True) # show the dataframe display(Mydataframe) |
Output:
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