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Python - How to Concatenate Two or More Pandas DataFrames along rows?
To concatenate two or more Pandas DataFrames along rows, use the pd.concat() method with axis=0. This combines DataFrames vertically, stacking them on top of each other. Syntax pd.concat([df1, df2, df3, ...], axis=0) Creating Sample DataFrames First, let's create three sample DataFrames to demonstrate concatenation − import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame({ "Col1": [10, 20, 30], "Col2": [40, 50, 60], "Col3": [70, 80, 90] }, index=[0, 1, 2]) print("DataFrame1:") print(dataFrame1) DataFrame1: ...
Read MoreHow to save Pandas data into Excel multiple sheets?
To save Pandas DataFrames into multiple Excel sheets, we can use the pd.ExcelWriter() method. This allows you to write multiple DataFrames to different sheets within a single Excel file. Make sure you have the openpyxl package installed before using ExcelWriter(). Basic Example Let's create two DataFrames and save them to different sheets in an Excel file − import pandas as pd # Create first DataFrame df1 = pd.DataFrame( [[5, 2], [4, 1]], index=["One", "Two"], columns=["Rank", "Subjects"] ) # Create second DataFrame df2 ...
Read MoreSelect DataFrame rows between two index values in Python Pandas
We can slice a Pandas DataFrame to select rows between two index values using Python's slice notation. This is useful when working with specific ranges of data in your DataFrame. Basic Index Slicing The simplest way to select rows between two index values is using the slice notation df[start:end] where the start is inclusive and end is exclusive ? import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], ...
Read MoreSelecting with complex criteria from a Pandas DataFrame
We can use different criteria to compare all the column values of a Pandas DataFrame. We can perform comparison operations like df[col] < 5, df[col] == 10, etc. For example, if we use the criteria 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. Basic Comparison Operations Example Let's create a DataFrame and apply various comparison criteria ? import pandas as pd df = pd.DataFrame( ...
Read MorePython Pandas – Check if two Dataframes are exactly same
The equals() method is used to check if two DataFrames are exactly the same. It compares both the structure and content of DataFrames, including data types, column names, and index values. Syntax DataFrame.equals(other) Parameters: other − Another DataFrame to compare with Returns: True if DataFrames are identical, False otherwise Example 1: Different DataFrames Let us create two different DataFrames and check if they are equal − import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], ...
Read MorePython Pandas – Find the Difference between two Dataframes
Finding differences between two DataFrames in Pandas involves comparing their structure, values, and content. The equals() method checks for exact equality, while other methods help identify specific differences. Creating Sample DataFrames Let's create two DataFrames to demonstrate comparison techniques ? import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] }) print("DataFrame1:") print(dataFrame1) DataFrame1: Car Units 0 ...
Read MoreGroup-by and Sum in Python Pandas
The groupby() and sum() functions in Pandas allow you to group data by specific columns and calculate the sum of numeric values for each group. This is particularly useful for data aggregation and analysis. Basic Group-by and Sum Here's how to group data by a single column and sum the values ? import pandas as pd # Create sample data df = pd.DataFrame({ "Category": ["A", "B", "A", "B", "A"], "Sales": [100, 150, 200, 120, 80], "Profit": [20, 30, 40, 25, 15] }) ...
Read MorePython – Create a Subset of columns using filter()
To create a subset of columns in Pandas, we can use the filter() method. This allows us to filter columns with similar patterns using the like parameter, or select specific columns using indexing. Creating a DataFrame First, let's create a sample DataFrame with product information ? import pandas as pd dataFrame = pd.DataFrame({ "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900] }) print("DataFrame...") print(dataFrame) DataFrame... Closing_Stock Opening_Stock ...
Read MoreHow to get column index from column name in Python Pandas?
To get column index from column name in Python Pandas, we can use the get_loc() method on the DataFrame's columns Index object. Syntax df.columns.get_loc(column_name) Parameters column_name − The name of the column whose index you want to find Return Value Returns an integer representing the positional index of the specified column. Example Let's create a DataFrame and find the index of different columns ? import pandas as pd # Create a DataFrame df = pd.DataFrame( { ...
Read MorePython program to construct Equidigit tuples
Equi-digit tuples are tuples where a number is split into two halves at the middle position. Python provides an efficient way to construct these using the floor division operator // and string slicing. Understanding Equi-digit Tuples An equi-digit tuple divides a number into two parts of equal or nearly equal length. For even-digit numbers, both parts have equal digits. For odd-digit numbers, the first part has one fewer digit than the second part. Example Below is a demonstration of constructing equi-digit tuples ? my_list = [5613, 1223, 966143, 890, 65, 10221] print("The list ...
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