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Technical articles with clear explanations and examples
Python - How to select a subset of a Pandas DataFrame
A Pandas DataFrame is a two-dimensional data structure that allows you to select specific subsets of data. You can select single columns, multiple columns, or rows based on conditions using various methods. Creating Sample Data Let's create a sample DataFrame to demonstrate subset selection ? import pandas as pd # Create sample data data = { 'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'], 'Reg_Price': [2500, 3500, 2500, 2000, 2500], 'Units': [100, 80, 120, 70, 110] } dataFrame = pd.DataFrame(data) print("Original DataFrame:") print(dataFrame) ...
Read MorePython - How to plot a Pandas DataFrame in a Bar Graph
A Pandas DataFrame can be easily visualized as a bar graph using the built-in plot() method. This is useful for comparing categorical data and displaying numerical relationships. Sample Dataset Let's create a sample DataFrame with car sales data ? import pandas as pd import matplotlib.pyplot as plt # Create sample data data = { 'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'], 'Reg_Price': [2000, 1500, 1500, 2000, 1500] } dataFrame = pd.DataFrame(data) print(dataFrame) Car Reg_Price 0 ...
Read MorePython Pandas - Plot multiple data columns in a DataFrame?
To plot multiple columns from a DataFrame, we use the plot() method with specific column selection. This is useful for comparing different data series visually using various chart types like bar graphs, line plots, and scatter plots. Import Required Libraries First, import pandas and matplotlib for data manipulation and plotting − import pandas as pd import matplotlib.pyplot as plt Creating Sample Data Let's create a DataFrame with cricket team rankings data − import pandas as pd import matplotlib.pyplot as plt # Sample cricket team data data = [["Australia", 2500, 85], ...
Read MorePython Pandas - Draw a Bar Plot and use median as the estimate of central tendency
A bar plot in Seaborn displays point estimates and confidence intervals as rectangular bars. You can use the estimator parameter in seaborn.barplot() to set median as the measure of central tendency instead of the default mean. Required Libraries Import the necessary libraries for creating bar plots with median estimation ? import seaborn as sns import pandas as pd import matplotlib.pyplot as plt import numpy as np Creating Sample Data Let's create sample cricket data to demonstrate median estimation in bar plots ? import seaborn as sns import pandas as pd import ...
Read MoreAppend list of dictionaries to an existing Pandas DataFrame in Python
To append a list of dictionaries to an existing Pandas DataFrame, you can use the pd.concat() method. The older append() method has been deprecated since Pandas 1.4.0. Creating the Initial DataFrame First, let's create a DataFrame with some initial data − import pandas as pd dataFrame = pd.DataFrame({ "Car": ['BMW', 'Audi', 'XUV', 'Lexus', 'Volkswagen'], "Place": ['Delhi', 'Bangalore', 'Pune', 'Chandigarh', 'Mumbai'], "Units": [100, 150, 50, 110, 90] }) print("Original DataFrame:") print(dataFrame) Original DataFrame: ...
Read MoreCreate a Pipeline and remove a column from DataFrame - Python Pandas
Use the ColDrop() method of pdpipe library to remove a column from Pandas DataFrame. The pdpipe library provides a pipeline-based approach for data preprocessing operations. Installing pdpipe First, install the pdpipe library ? pip install pdpipe Importing Required Libraries Import the required pdpipe and pandas libraries with their respective aliases ? import pdpipe as pdp import pandas as pd Creating a DataFrame Let us create a DataFrame with car data. Here, we have two columns ? import pandas as pd dataFrame = pd.DataFrame({ ...
Read MorePython - Compute first of group values in a Pandas DataFrame
To compute the first occurrence of group values in a Pandas DataFrame, use the groupby().first() method. This returns the first non-null value for each group, which is useful for data aggregation and analysis. Syntax DataFrame.groupby(by).first() Creating a Sample DataFrame Let's create a DataFrame with car data to demonstrate grouping ? import pandas as pd dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'BMW', 'Tesla', 'Lexus', 'Tesla'], "Place": ['Delhi', 'Bangalore', 'Pune', 'Punjab', 'Chandigarh', 'Mumbai'], "Units": [100, 150, 50, 80, 110, 90] }) ...
Read MoreHow to extract the value names and counts from value_counts() in Pandas?
The value_counts() method in Pandas returns a Series with unique values and their frequencies. To extract the value names and counts separately, you can use the index and values attributes of the resulting Series. Creating a DataFrame First, let's create a sample DataFrame with car data ? import pandas as pd # Creating dataframe dataFrame = pd.DataFrame({ "Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'], "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000], "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500], ...
Read MorePython Pandas – Merge and create cartesian product from both the DataFrames
To merge Pandas DataFrames and create a cartesian product, use the merge() function with the how="cross" parameter. A cartesian product combines every row from the first DataFrame with every row from the second DataFrame. Syntax pd.merge(df1, df2, how="cross") Creating Sample DataFrames Let's create two DataFrames to demonstrate the cartesian product ? import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame({ "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 120] }) print("DataFrame1:") print(dataFrame1) # Create DataFrame2 dataFrame2 = pd.DataFrame({ ...
Read MorePython Pandas – Check for Null values using notnull()
The notnull() method in Pandas returns a Boolean DataFrame where True indicates non-null values and False indicates null (NaN) values. This method is essential for identifying missing data in your DataFrame. Syntax DataFrame.notnull() Creating Sample Data Let's create a DataFrame with some null values to demonstrate notnull() ? import pandas as pd import numpy as np # Create sample data with null values data = { 'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes'], 'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', None], 'UnitsSold': ...
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