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Articles by Rishikesh Kumar Rishi
Page 13 of 102
How to find numeric columns in Pandas?
To find numeric columns in Pandas, we can use the select_dtypes() method to filter columns based on their data types. This method allows us to specify which numeric types to include or exclude from our DataFrame. Basic Example Let's start with a simple example using select_dtypes() ? import pandas as pd # Create a DataFrame with mixed data types df = pd.DataFrame({ 'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'], 'marks': [89, 23, 100, 56, 90], 'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"] }) print("Input ...
Read MorePython Pandas – Find the maximum value of a column and return its corresponding row values
To find the maximum value of a column and return its corresponding row values in Pandas, we can use df.loc[df[col].idxmax()]. This method first finds the index of the maximum value using idxmax(), then uses loc[] to retrieve the entire row. Syntax df.loc[df[column_name].idxmax()] Where: df − The DataFrame column_name − The column to find the maximum value in idxmax() − Returns the index of the maximum value loc[] − Selects the row by index Example Let's create a DataFrame and find the maximum values for different columns ? import pandas ...
Read MoreHow to get the correlation between two columns in Pandas?
We can use the .corr() method to get the correlation between two columns in Pandas. The correlation coefficient measures the linear relationship between two variables, ranging from -1 to 1. Basic Syntax # Method 1: Using .corr() on a Series correlation = df['column1'].corr(df['column2']) # Method 2: Using .corr() on DataFrame to get correlation matrix correlation_matrix = df[['column1', 'column2']].corr() Example Let's create a DataFrame and calculate correlations between different columns ? import pandas as pd # Create sample DataFrame df = pd.DataFrame({ "x": [5, 2, 7, 0], ...
Read MoreHow to filter rows in Pandas by regex?
A regular expression (regex) is a sequence of characters that define a search pattern. Pandas provides several methods to filter DataFrame rows using regex patterns, including str.match(), str.contains(), and str.extract(). Using str.match() Method The str.match() method matches regex patterns from the beginning of each string ? import pandas as pd df = pd.DataFrame({ 'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'], 'marks': [89, 23, 100, 56, 90], 'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"] }) print("Input DataFrame:") print(df) Input DataFrame: ...
Read MorePython – Pandas Dataframe.rename()
The rename() method in Pandas allows you to change column names in a DataFrame efficiently. You can rename single or multiple columns using dictionary mapping or functions. Basic Syntax The basic syntax for renaming columns is ? DataFrame.rename(columns={old_name: new_name}, inplace=False) Renaming a Single Column Here's how to rename one column using the rename() method ? import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, ...
Read MoreHow to access a group of rows in a Pandas DataFrame?
To access a group of rows in a Pandas DataFrame, we can use the loc[] indexer. For example, if we use df.loc[2:5], then it will select all the rows from index 2 to 5 (inclusive). Using loc[] for Row Selection The loc[] method allows label-based indexing and supports slice notation for selecting consecutive rows ? import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], "y": [4, 7, 5, 1, 5, 1, 4, ...
Read MoreDelete the first three rows of a DataFrame in Pandas
To delete the first three rows of a DataFrame in Pandas, we can use the iloc[] indexer to slice the DataFrame starting from the fourth row (index 3). Using iloc[] to Delete First Three Rows The iloc[] method allows positional indexing. By using df.iloc[3:], we select all rows starting from index 3 onwards ? import pandas as pd # Create a DataFrame df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], ...
Read MoreHow to convert a DataFrame into a dictionary in Pandas?
To convert a Pandas DataFrame into a dictionary, we can use the to_dict() method. This method offers several orientation options to structure the output dictionary differently based on your needs. Basic DataFrame to Dictionary Conversion Let's start with a simple example using the default orientation ? import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) ...
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], ...
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