Find percentile stats of a given column in python

Find percentile stats of a given column in python

To find percentile statistics of a given column in Python, you can use libraries like NumPy or pandas. Here's how you can do it using both libraries:

Using NumPy:

NumPy provides the percentile() function to calculate percentiles of a dataset.

import numpy as np data = [20, 25, 30, 35, 40, 45, 50, 55, 60, 65] percentiles = [25, 50, 75] percentile_values = np.percentile(data, percentiles) for p, value in zip(percentiles, percentile_values): print(f"{p}th percentile: {value}")

Replace data with your own dataset and adjust the percentiles list as needed.

Using pandas:

If your data is in a pandas DataFrame, you can use the describe() method to get percentile statistics.

import pandas as pd data = {'column_name': [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]} df = pd.DataFrame(data) percentiles = [0.25, 0.5, 0.75] # Equivalent to 25th, 50th, and 75th percentiles percentile_stats = df['column_name'].describe(percentiles=percentiles) print(percentile_stats)

Replace 'column_name' with the actual column name in your DataFrame.

In both cases, the output will provide percentile statistics for the given column:

  • 25th percentile: The value below which 25% of the data falls.
  • 50th percentile: The value below which 50% of the data falls (also known as the median).
  • 75th percentile: The value below which 75% of the data falls.

Choose the method that suits your data format and analysis needs.

Examples

  1. How to calculate percentile in Python Pandas DataFrame?

    • Description: This query is about calculating percentiles for a specific column within a Pandas DataFrame using built-in functions.
    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Calculate 75th percentile for column 'A' percentile_75 = df['A'].quantile(0.75) print("75th percentile:", percentile_75)

  2. Python code to find percentile of a column in a CSV file

    • Description: This query pertains to reading a CSV file into a Pandas DataFrame and then computing percentiles for a specific column.
    import pandas as pd # Read CSV into DataFrame df = pd.read_csv('your_file.csv') # Calculate 90th percentile for column 'column_name' percentile_90 = df['column_name'].quantile(0.90) print("90th percentile:", percentile_90)

  3. How to find percentile using NumPy in Python?

    • Description: This query focuses on utilizing NumPy, a numerical computing library, to calculate percentiles in Python.
    import numpy as np # Sample data data = np.array([1, 2, 3, 4, 5]) # Calculate 50th percentile (median) percentile_50 = np.percentile(data, 50) print("50th percentile (median):", percentile_50)

  4. Python code to compute quartiles of a column in DataFrame

    • Description: This query seeks code to compute quartiles (including median) for a specific column within a Pandas DataFrame.
    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Compute quartiles for column 'A' quartiles = df['A'].quantile([0.25, 0.5, 0.75]) print("Quartiles:") print(quartiles)

  5. How to find percentile rank of values in a Python list?

    • Description: This query focuses on determining the percentile rank of individual values within a Python list.
    import numpy as np # Sample data data = [1, 2, 3, 4, 5] # Calculate percentile rank of value 3 percentile_rank = np.percentile(data, 3) print("Percentile rank of value 3:", percentile_rank)

  6. Python code to calculate interquartile range (IQR) of a DataFrame column

    • Description: This query seeks code to compute the interquartile range (IQR) for a specific column within a Pandas DataFrame.
    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Calculate interquartile range for column 'A' Q1 = df['A'].quantile(0.25) Q3 = df['A'].quantile(0.75) IQR = Q3 - Q1 print("Interquartile range:", IQR)

  7. How to find percentile using scipy.stats in Python?

    • Description: This query involves utilizing the scipy.stats module to compute percentiles in Python.
    from scipy import stats # Sample data data = [1, 2, 3, 4, 5] # Calculate 80th percentile using scipy.stats percentile_80 = stats.scoreatpercentile(data, 80) print("80th percentile:", percentile_80)

  8. Python code to find percentile of a column in SQLite database

    • Description: This query relates to querying an SQLite database to calculate percentiles for a specific column.
    import sqlite3 # Connect to SQLite database conn = sqlite3.connect('your_database.db') # Query to calculate 95th percentile for column 'column_name' query = "SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY column_name) FROM your_table;" # Execute query result = conn.execute(query).fetchone()[0] print("95th percentile:", result)

  9. How to compute deciles in Python Pandas DataFrame?

    • Description: This query revolves around computing deciles (percentiles divided into ten equal parts) for a column in a Pandas DataFrame.
    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Compute deciles for column 'A' deciles = df['A'].quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]) print("Deciles:") print(deciles)

  10. Python code to calculate weighted percentile in a DataFrame column

    • Description: This query is about calculating weighted percentiles for a specific column within a Pandas DataFrame, considering the weights assigned to each value.
    import numpy as np import pandas as pd # Sample DataFrame with 'values' and 'weights' columns df = pd.DataFrame({'values': [1, 2, 3, 4, 5], 'weights': [0.1, 0.2, 0.3, 0.2, 0.2]}) # Calculate weighted percentile (e.g., 80th percentile) percentile = 80 weighted_percentile = np.percentile(df['values'], percentile, interpolation='linear', weights=df['weights']) print(f"{percentile}th percentile (weighted):", weighted_percentile)

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