Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental, high-level building block for doing practical, real-world data analysis in Python.
The two primary data structures of Pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame provides everything about R’s data.frame provides, and much more. Pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other third-party libraries.
Data structures
Dimension |
Name |
Description |
---|---|---|
1 |
Series |
1D-labeled homogeneously-typed array |
2 |
DataFrame |
General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column |
Reading Tabular Data
Pandas provides the read_csv() function to read data stored as a csv file into a pandas DataFrame. pandas supports many different file formats or data sources out of the box (csv, excel, sql, json, parquet, …), each of them with the prefix read_*.
Importing Necessary libraries
import pandas as pd
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CSV file
1. Reading the csv file
Dataset link : dataset.csv
# Load the dataset from the 'dataset.csv' file using Pandas data = pd.read_csv( 'dataset.csv' )
# Display the first few rows of the loaded dataset print (data.head())
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Output:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
2. Reading excel file
Dataset link : data.xlsx
# Load the dataset from the 'data.xlsx' file using Pandas data = pd.read_excel( 'data.xlsx' )
# Display the first few rows of the loaded dataset print (data.head())
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Output:
Column1 Column2 Column3
0 1 A 10.5
1 2 B 20.3
2 3 C 15.8
3 4 D 8.2
Writing Tabular Data
1. Writing in Excel file
# Reading the data from a CSV file named 'dataset.csv' into a pandas DataFrame data = pd.read_csv( 'dataset.csv' )
# Specifying the path for the new Excel file to be created excel_file_path = 'newDataset.xlsx'
# Writing the DataFrame to an Excel file with the specified path, excluding the index column data.to_excel(excel_file_path, index = False )
# Displaying a message indicating that the data has been successfully written to the Excel file print (f 'Data written to Excel file: {excel_file_path}' )
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Output:
Data written to Excel file: newDataset.xlsx
2. Writing in CSV file
# Reading the data from a CSV file named 'dataset.csv' into a pandas DataFrame data = pd.read_csv( 'dataset.csv' )
# Specifying the path for the new CSV file to be created csv_file_path = 'newDataset.csv'
# Writing the DataFrame to a CSV file with the specified path, excluding the index column data.to_csv(csv_file_path, index = False )
# Displaying a message indicating that the data has been successfully written to the CSV file print (f 'Data written to CSV file: {csv_file_path}' )
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Output:
Data written to CSV file: newDataset.csv
Conclusion
In conclusion, Pandas provides essential tools for efficiently managing tabular data, allowing seamless reading and writing operations across various file formats. The library’s key functions, such as read_csv, read_excel, to_csv, and to_excel, facilitate the smooth import and export of data, irrespective of its original format.
Pandas’ adaptability extends to diverse data scenarios, enabling users to address nuances like missing values and customizable parameters. Whether dealing with CSV, Excel, SQL, JSON, or other file types, Pandas offers a consistent and user-friendly interface for data manipulation.