Mode is the value that appears the most in a set of values. Use the fillna() method and set the mode to fill missing columns with mode. At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame with ... Read More

We can search DataFrame for a specific value. Use iloc to fetch the required value and display the entire row. At first, import the required library −import pandas as pdCreate a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, ... Read More

The sort_index() is used to sort index in ascending and descending order. If you won’t mention any parameter, then index sorts in ascending order.At first, import the required library −import pandas as pdCreate a new DataFrame. It has unsorted indexes −dataFrame = pd.DataFrame([100, 150, 200, 250, 250, 500], index=[4, 8, ... Read More

To add a prefix to all the column names, use the add_prefix() method. At first, import the required Pandas library −import pandas as pdCreate a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, ... Read More

To reverse the column order, use the dataframe.columns and set as -1 −dataFrame[dataFrame.columns[::-1]At first, import the required library −import pandas as pd Create a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, ... Read More

To remove a column with all null values, use the dropna() method and set the “how” parameter to “all” −how='all'At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame. We have set the NaN values using the Numpy ... Read More

To fetch only capital words, we are using regex. The re module is used here and imported. Let us import all the libraries −import re import pandas as pdCreate a DataFrame −data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ['KEYBOARD', 'charger', 'SMARTTV', 'camera']] dataFrame = pd.DataFrame(data)Now, extract capital words −for ... Read More

Use the isin() method to display True for infinite values. At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a dictionary of list. We have set the infinity values using the Numpy np.inf −d = { "Reg_Price": [7000.5057, np.inf, ... Read More

To check and display row index, use the isinf() with any(). At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a dictionary of list. We have set the infinity values using the Numpy np.inf −d = { "Reg_Price": [7000.5057, ... Read More

To count the observations, first use the groupby() and then use count() on the result. At first, import the required library −dataFrame = pd.DataFrame({'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'], 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, 25, 50]})Group the ... Read More