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 2 columns. We have set the NaN values using the Numpy np.NaN −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'], "Units": [100, 150, np.NaN, 80, np.NaN, np.NaN] } )Find mode of the column values with NaN i.e, for Units columns ... 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, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, 120, 150, 110, 200, 250] })Let’s search Car with Registeration Price 500 −for i in range(len(dataFrame.Car)): if 5000 == dataFrame.Reg_Price[i]: indx = iNow, display the found value −dataFrame.iloc[indx] ExampleFollowing is ... 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, 2, 9, 15, 11],columns=['Col1'])Sort the indexes −dataFrame.sort_index() ExampleFollowing is the code −import pandas as pd dataFrame = pd.DataFrame([100, 150, 200, 250, 250, 500],index=[4, 8, 2, 9, 15, 11],columns=['Col1']) print"DataFrame...",dataFrame print"Sort index...",dataFrame.sort_index()OutputThis will produce the following output −DataFrame...    Col1 4 100 8 150 2 200 9 250 15 250 11 500 Sort index... Col1 2 200 4 100 8 150 9 250 11 500 15 250

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, 9000, 6000], "Units_Sold": [ 100, 120, 150, 110, 200, 250] })Add a prefix to _column to every column using add_prefix() −dataFrame.add_prefix('column_') ExampleFollowing is the code −import pandas as pd # creating dataframe dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": ... 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, 6000], "Units_Sold": [ 100, 120, 150, 110, 200, 250] })Reverse the column order −df = dataFrame[dataFrame.columns[::-1]] ExampleFollowing is the code −import pandas as pd # creating dataframe 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 np.infdataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Robin', 'Scarlett', 'Kat', 'Ted'], "Result": [np.NAN, np.NAN, np.NAN, np.NAN, np.NAN, np.NAN, np.NAN] } )To remove a column with all null values, use dropna() and set the required parameters −dataFrame.dropna(how='all', axis=1, inplace=True) ExampleFollowing is the code ... 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 i in range(dataFrame.shape[1]): for ele in dataFrame[i]: if bool(re.match(r'\w*[A-Z]\w*', str(ele))): print(ele)ExampleFollowing is the code −import re import pandas as pd # create a dataframe data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ... 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, 5000, np.inf, 9000.75768, 6000, 900, np.inf] } Creating DataFrame from the above dictionary of list −dataFrame = pd.DataFrame(d)Display True for infinite values −res = dataFrame.isin([np.inf, -np.inf]) ExampleFollowing is the code −import pandas as pd import numpy as np # dictionary of list d = { "Reg_Price": [7000.5057, np.inf, 5000, ... 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, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf] } Creating DataFrame from the above dictionary of list −dataFrame = pd.DataFrame(d)Getting row index with infinity values −indexNum = dataFrame.index[np.isinf(dataFrame).any(1)] ExampleFollowing is the code −import pandas as pd import numpy as np # dictionary of list d = { "Reg_Price": [7000.5057, np.inf, ... 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 column with duplicate values −group = dataFrame.groupby("Product Category") Get the count −group.count()ExampleFollowing is the code −import pandas as pd # create a dataframe 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, ... Read More