/EDA_wtih_pandas

Exploratory Data Analysis (EDA) with Pandas can help guide users or collaborators on how to perform EDA on a dataset

Primary LanguageJupyter Notebook

EDA_with_Pandas.ipynb:

This Jupyter Notebook contains a step-by-step guide on performing EDA with Pandas. It covers data loading, data cleaning, data visualization, and basic statistical analysis.

Dataset Details

  • File Name: [telecom_churn.csv]
  • File Format: [CSV]

In this project, we performed Exploratory Data Analysis (EDA) using the Pandas library in Python. Here are some of the key Pandas functions and methods used for EDA:

  • pd.read_csv(): Used to read the dataset from a CSV file into a Pandas DataFrame.
  • .head(): Used to display the first few rows of the dataset.
  • .info(): Used to get information about the dataset, including data types and missing values.
  • .describe(): Used to generate summary statistics of numeric columns.
  • .value_counts(): Used to count the occurrences of unique values in categorical columns.
  • .groupby(): Used to group data by a specific column for aggregation.
  • .plot(): Used to create various plots and visualizations, such as bar plots and histograms.

These Pandas functions and methods helped us gain insights into the dataset, identify trends, and make data-driven decisions.