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Simple & Easy-to-use python modules to perform Quick Exploratory Data Analysis for any structured dataset!

Project description


Simple & Easy-to-use python modules to perform Quick Exploratory Data Analysis for any structured dataset!


Getting Started

You will need to have Python 3 and Jupyter Notebook installed in your local system. Once installed, clone this repository to your local to get the project structure setup.

git clone

You will also need to install few python package dependencies in your evironment to get started. You can do this by:

pip3 install -r requirements.txt

OR you can also install the package from PyPi Index using the pip installer:

pip3 install quickda

Table of Contents

  1. Data Exploration - explore(data)

    • data: pd.DataFrame
    • method: string, default="summarize"
      • "summarize" : Generates a summary statistics of the dataset
      • "profile" : Generates a HTML Report of the Dataset Profile
    • report_name: string, default="Dataset Report"
      • Parameter to customise the generated report name
    • is_large_dataset: Boolean, default=False
      • Parameter set to True explicitly to flag, in case of a large dataset
  2. Data Cleaning - clean(data) : [Returns DataFrame]

    • data: pd.DataFrame
    • method: string, default="default"
      • "default" : Standardizes column names, Removes duplicates rows and Drops missing values
      • "standardize" : Standardizes column names
      • "dropcols" : Drops columns specified by the user
      • "duplicates" : Removes duplicate rows
      • "replaceval" : Replaces a value in dataframe with new value specified by the user
      • "fillmissing" : Interpolates all columns with missing values using forward filling
      • "dropmissing" : Drops all rows with missing values
      • "cardinality" : Reduces Cardinality of a column given a threshold
      • "dtypes" : Explicitly converts the Data Types as specified by the user
      • "outliers" : Removes all outliers in data using IQR method
    • columns: list/string, default=[]
      • Parameter to specify column names in the DataFrame
    • dtype: string, default="numeric"
      • "numeric" : Converts columns dtype to numeric
      • "category" : Converts columns dtype to category
      • "datetime" : Converts columns dtype to datetime
    • to_replace: string/integer/regex, default=""
      • Parameter to pass a value to replace in the DataFrane
    • value: string/integer/regex, default=np.nan
      • Parameter to pass a new value that replaces an old value in the Dataframe
    • threshold: float, default=0
      • Parameter to set threshold in the range of [0,1] for cardinality
  3. EDA Numerical Features - eda_num(data)

    • data: pd.DataFrame
    • method: string, default="default"
      • "default" : Shows all Outlier & Distribution Analysis via BoxPlots & Histograms
      • "correlation" : Gets the correlation matrix between all numerical features
    • bins: integer, default=10
      • Parameter to set the number of bins while displaying histograms
  4. EDA Categorical Features - eda_cat(data, x)

    • data: pd.DataFrame
    • x: string, First Categorical Type Column Name
    • y: string, default=None
      • Parameter to pass the Second Categorical Type Column Name
    • method: string, default="default"
      • "default" : Shows category count plot & summarizes it in a frequency table
  5. EDA Numerical with Categorical Features - eda_numcat(data, x, y)

    • data: pd.DataFrame
    • x: string/list, Numeric/Categorical Type Column Name(s)
    • y: string/list, Numeric/Categorical Type Column Name(s)
    • method: string, default="pps"
      • "pps" : Calculates Predictive Power Score Matrix
      • "relationship" : Shows Scatterplot of given features
      • "comparison" : Shows violin plots to compare categories across numerical features
      • "pivot" : Generates pivot table using column names, values and aggregation function
    • hue: string, default=None
      • Parameter to visualise a categorical Type feature within scatterplots
    • values: string/list, default=None
      • Parameter to set columns to aggregate on pivot views
    • aggfunc: string, default="mean"
      • Parameter to set aggregate functions on pivot tables
      • Example: 'min', 'max', 'mean', 'median', 'sum', 'count'
  6. EDA Time Series Data - eda_timeseries(data, x, y)

    • data: pd.DataFrame
    • x: string, Datetime Type Column Name
    • y: string, Numeric Type Column Name

Upcoming Work

  1. Basic Preprocessing for Text Data - Tokenization, Normalization, Noise Removal, Lemmatization
  2. EDA for Text Data - NGrams, POS tagging, Word Cloud, Sentiment Analysis
  3. Quick Insight Generation for all EDA steps - Generate easy-to-read textual insights

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