eda-report 2.2.4

A simple program to automate exploratory data analysis and reporting.

eda-report - Automated Exploratory Data Analysis

A Python program to help automate the exploratory data analysis and reporting process.

Input data is processed and analysed using pandas' built-in methods, and graphs are plotted using matplotlib & seaborn. The results are then nicely packaged as a Word (.docx) document using python-docx.

Installation

You can install the package from PyPI using:

pip install eda-report

Basic Usage

1. Graphical User Interface

The eda-report command launches a graphical window to help select and analyse a csv/excel file:

eda-report

You will be prompted to set a report title, target variable (optional), graph color and output filename, after which the contents of the input file will be analysed, and the results will be saved in a Word (.docx) document.

NOTE: For help with Tk - related issues, consider visiting TkDocs.

2. Command Line Interface

To analyse a file named input.csv, just supply its path to the eda-report command:

eda-report -i input.csv

Or even:

eda-report -i input.csv -o output.docx -c cyan --title 'EDA Report'

For more details on the optional arguments, pass the -h or --help flag to view the help message:

eda-report -h

usage: eda-report [-h] [-i INFILE] [-o OUTFILE] [-t TITLE] [-c COLOR]
                  [-T TARGET]

Automatically analyse data and generate reports. A graphical user interface
will be launched if none of the optional arguments is specified.

optional arguments:
  -h, --help            show this help message and exit
  -i INFILE, --infile INFILE
                        A .csv or .xlsx file to analyse.
  -o OUTFILE, --outfile OUTFILE
                        The output name for analysis results (default: eda-
                        report.docx)
  -t TITLE, --title TITLE
                        The top level heading for the report (default:
                        Exploratory Data Analysis Report)
  -c COLOR, --color COLOR
                        The color to apply to graphs (default: cyan)
  -T TARGET, --target TARGET
                        The target variable (dependent feature), used to
                        color-code plotted values. An integer value is treated
                        as a column index, whereas a string is treated as a
                        column label.

3. Interactive Mode

3.1 Analyse univariate data

>>> from eda_report.univariate import Variable
>>> Variable(range(20), name="1 to 20")
        Overview
        ========
Name: 1 to 20
Type: numeric
Unique Values: 20 -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, [...]
Missing Values: None
          ***
      Summary Statistics
                         1 to 20
Number of observations  20.00000
Average                  9.50000
Standard Deviation       5.91608
Minimum                  0.00000
Lower Quartile           4.75000
Median                   9.50000
Upper Quartile          14.25000
Maximum                 19.00000
Skewness                 0.00000
Kurtosis                -1.20000

3.2 Analyse multivariate data

>>> from eda_report.multivariate import MultiVariable
>>> from seaborn import load_dataset
>>> data = load_dataset("iris")
>>> MultiVariable(data)
                        OVERVIEW
                        ========
Numeric features: sepal_length, sepal_width, petal_length, petal_width
Categorical features: species
                          ***
          Summary Statistics (Numeric features)
          -------------------------------------
              count    mean     std  min  25%   50%  75%  max  skewness  kurtosis
sepal_length  150.0  5.8433  0.8281  4.3  5.1  5.80  6.4  7.9    0.3149   -0.5521
sepal_width   150.0  3.0573  0.4359  2.0  2.8  3.00  3.3  4.4    0.3190    0.2282
petal_length  150.0  3.7580  1.7653  1.0  1.6  4.35  5.1  6.9   -0.2749   -1.4021
petal_width   150.0  1.1993  0.7622  0.1  0.3  1.30  1.8  2.5   -0.1030   -1.3406
                          ***
          Summary Statistics (Categorical features)
          -----------------------------------------
        count unique     top freq relative freq
species   150      3  setosa   50        33.33%
                          ***
          Bivariate Analysis (Correlation)
          --------------------------------
petal_length & petal_width --> very strong positive correlation (0.96)
sepal_length & petal_length --> strong positive correlation (0.87)
sepal_length & petal_width --> strong positive correlation (0.82)
sepal_length & sepal_width --> very weak negative correlation (-0.12)
sepal_width & petal_length --> weak negative correlation (-0.43)
sepal_width & petal_width --> weak negative correlation (-0.37)

3.3 Generate a report

>>> from eda_report import get_word_report
>>> from seaborn import load_dataset

>>> data = load_dataset("iris")
>>> get_word_report(data)
Bivariate analysis: 100%|███████████████████████████████████| 6/6 numeric pairs.
Univariate analysis: 100%|███████████████████████████████████| 5/5 features.
[INFO 17:31:37.880] Done. Results saved as 'eda-report.docx'
<eda_report.document.ReportDocument object at 0x7f3040c9bcd0>

Visit the official documentation for more details.