Pseudo Code for Pandas DataFrame Manipulation

Objective

This pseudo code outlines the steps to perform common operations on a DataFrame using the Pandas library in Python. The logic involves creating a DataFrame, manipulating data, and performing analysis.

Sections

1. Import Necessary Libraries

• Import the Pandas library to utilize its data manipulation capabilities.

IMPORT pandas as pd

2. Create a DataFrame

• Define a dictionary with sample data.
• Convert the dictionary into a Pandas DataFrame.

DEFINE data AS {
    'Column1': [value1, value2, value3],
    'Column2': [value4, value5, value6],
    'Column3': [value7, value8, value9]
}
CREATE DataFrame df FROM data

3. Display the DataFrame

• Print the contents of the DataFrame for verification.

PRINT df

4. DataFrame Manipulation

4.1. Add a New Column

• Create a new column based on existing data.

df['NewColumn'] = df['Column1'] + df['Column2']

4.2. Filter Rows

• Filter the DataFrame to include only rows meeting specific criteria.

FILTERED_df = df WHERE df['Column1'] > threshold

4.3. Sort Data

• Sort the DataFrame by a specific column in ascending order.

SORTED_df = df SORT BY 'Column2' IN ascending order

4.4. Group Data

• Group data by a specific column and calculate the mean of another column.

GROUPED_df = df GROUP BY 'Column3' CALCULATE mean OF 'NewColumn'

5. Data Analysis

• Perform calculations or aggregations on the DataFrame.

5.1. Calculate Mean

• Calculate the mean of a specific column.

mean_value = MEAN(df['NewColumn'])

5.2. Summary Statistics

• Generate summary statistics for the DataFrame.

summary = df DESCRIBE

6. Export DataFrame

• Save the manipulated DataFrame to a CSV file.

EXPORT df TO 'output_file.csv' WITH index FALSE

Conclusion

This pseudo code serves as a guide for performing common Pandas operations, facilitating the understanding of DataFrame manipulation in a structured manner. Each section addresses a specific aspect of data handling, such as creation, manipulation, analysis, and export. For further training on data manipulation techniques, consider exploring the resources available on the Enterprise DNA platform.