To put a Pandas DataFrame into a JSON file and read it again, we can use to_json() and read_json() methods.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use to_json() method to dump the DataFrame into a JSON file.Use read_json() method to read the JSON file.Exampleimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 7, 0],       "y": [4, 7, 5, 1],       "z": [9, 3, 5, 1] } ) print "Input DataFrame is:", df print "JSON output for input DataFrame: ", df.to_json("test.json") ... Read More

To concatenate more than two Pandas DataFrames, use the concat() method. Set the axis parameter as axis = 0 to concatenate along rows. At first, import the required library −import pandas as pdLet us create the 1st DataFrame −dataFrame1 = pd.DataFrame(    {       "Col1": [10, 20, 30], "Col2": [40, 50, 60], "Col3": [70, 80, 90], }, index=[0, 1, 2], ) Let us create the 2nd DataFrame −dataFrame2 = pd.DataFrame(    {       "Col1": [100, 110, 120], "Col2": [130, 140, 150], "Col3": [160, 170, 180], }, ... Read More

To save Pandas DataFrames into multiple excel sheets, we can use the pd.ExcelWriter() method. Make sure you have the openpyxl package installed before using ExcelWriter().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df1.Print the input DataFrame, df1.Create another DataFrame, df2, and print it.Use ExcelWriter() method to write the given formatted cells into an Excel sheet.Exampleimport pandas as pd df1 = pd.DataFrame( [[5, 2], [4, 1]], index=["One", "Two"], columns=["Rank", "Subjects"] ) df2 = pd.DataFrame( [[15, 21], [41, 11]], index=["One", "Two"], columns=["Rank", ... Read More

We can slice a Pandas DataFrame to select rows between two index values. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable for lower limit of the index.Initialize another variable for upper limit of the index.Use df[index_lower_limit: index_upper_limit] to print the DataFrame in range index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, ... Read More

We can use different criteria to compare all the column values of a Pandas DataFrame. We can perform comparison operations like df[col]2, then it will check all the values from col and compare whether they are greater than 2. For all the column values, it will return True if the condition holds, else False. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col, with a column name.Perform some comparison operations.Print the resultant DataFrame.Example import pandas as pd df = pd.DataFrame( ... Read More

To concatenate more than two Pandas DataFrames, use the concat() method. Set the axis parameter as axis = 1 to concatenate along columns. At first, import the required library −import pandas as pdLet us create the 1st DataFrame −dataFrame1 = pd.DataFrame(    { "Col1": [10, 20, 30], "Col2": [40, 50, 60], "Col3": [70, 80, 90],    }, index=[0, 1, 2], )Let us create the 2nd DataFrame −dataFrame2 = pd.DataFrame(    {       "Col1": [100, 110, 120], "Col2": [130, 140, 150], "Col3": [160, 170, 180], }, ... Read More

The equals() function is used to check if two dataframes are exactly same. At first, let us create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] ... Read More

To count unique values per groups in Python Pandas, we can use df.groupby('column_name').count().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use df.groupby('rank')['id'].count() to find the count of unique values per groups and store it in a variable "count".Print the count from Step 3.Exampleimport pandas as pd df = pd.DataFrame(     {        "id": [1, 2, 1, 3, 5, 1, 4, 3, 6, 7],        'rank': [1, 4, 1, 2, 1, 4, 6, 1, 5, 3]     } ) print"Input DataFrame 1 is:", df count = df.groupby('rank')['id'].count() print"Frequency of ranks:", countOutputInput DataFrame 1 is:    id  rank 0   1    1 1   2    4 2   1    1 3   3    2 4   5    1 5   1    4 6   4    6 7   3    1 8   6    5 9   7    3 Frequency of ranks: rank 1  4 2  1 3  1 4  2 5  1 6  1 Name: id, dtype: int64

To find the difference between two DataFrame, you need to check for its equality. Also, check the equality of columns.Let us create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame(    { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } ... Read More

To find group-by and sum in Python Pandas, we can use groupby(columns).sum().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Find the groupby sum using df.groupby().sum(). This function takes a given column and sorts its values. After that, based on the sorted values, it also sorts the values of other columns.Print the groupby sum.Exampleimport pandas as pd df = pd.DataFrame(     {        "Apple": [5, 2, 7, 0],        "Banana": [4, 7, 5, 1],        "Carrot": [9, 3, 5, 1]     } ) print "Input DataFrame 1 ... Read More