Throughout my data science journey, I have learned that it is a good practice to understand the data first and try to gather as many insights from it. Exploratory Data Analysis (EDA) is all about making sense of data in hand, before getting dirty with machine learning and sophisticated algorithm.

While there are plenty of Python libraries that can help create beautiful and complex visualizations, I often find myself starting with the most simplistic analyses: count plot, histogram, scatter plot, boxplot, etc. This initial EDA workflow is very similar for each new data set. But unfortunately, they are tedious. Converting to correct data types, selecting the right variable type for the right plot, iterate through all possible variable combinations, adjusting plot aesthetics and labels, etc. These are the tasks I would love to do... once. As someone that does not find great joy in completing repetitive tasks, I set out to build a tool that allows me to be as lazy as possible.

Majora is a python library that automates common tasks in your exploratory data analysis. This includes missing values visualization, missing values handling, variable types handling, predictive modeling, and a variety of univariate and bivariate graphs. The goal is to provide a fast and effective tool for discovering insights, so you can quickly move on to the machine learning model.

• Smart data type conversion
• Automatic graph discovery
• Simple missing values identificaiton and handling
• CART model with cross-validation and tree visualization

• Table of contents
• Installation
• Dataset Overview
• Missing Values
  • Identify Missing Values
  • Handle Missing Values
• Variable Types Handling
  • Identify data types
  • Type conversions
• Visualization
  • Univariate plots
    • Histogram
    • Count Plots
    • Word Cloud
  • Bivariate plots
    • Correlation plots
    • Principal Component Analysis
    • Box Plots
    • Relative Frequency Plots
    • Correspondence Analysis
    • Trend plot
• Decision Tree Visualizer

⚠️ Majora is only compatible with Python 3.

⚠️ Decision Tree visualizer requires graphviz.

Install Via PIP

> pip install majora 

from majora import majora

Initiate a class instance with input dataframe:

heart = pd.read_csv('datasets/heart.csv')
heart['target'] = np.where(heart['target'] == 1, 'has disease', 'no disease')

report = majora.auto_eda(heart, target_variable = 'target')

The available parameters are:

• df: the input pandas dataframe.
• target_variable: the target variable that Auto_EDA will focus on.

report.get_samples()

get_samples() returns a df concatenated from head + random samples + tail of the dataset.

>>> report.get_overview()

Number of Variables: 303
Number of Observations: 14
Memory Usage: 0.052106 Mb

get_samples() returns number of variables, observations, and memory usage.

report.get_missings(missing_tag= '?')

The available parameters are:

• missing_tag: Sometimes missing values are denoted with a number or string (eg. '?'), enter the missing tag to replace them with NAs

>>> report.handle_missings(strategy = 'deletion', drop_threshold = 0.5)

Dropped columns: ['NMHC(GT)']
Number of dropped rows: 2416 --> 25.8% of rows removed

The available parameters are:

• strategy: select a strategy to handle missing data. Options: 'deletion', 'encode', 'mean_mode'

'deletion': drop variables with > 70% missing (or a different threshold using argument 'drop_threshold') and remove observations that contain at least 1 missing value.

'encode'(Encoding imputation): for numerical variable, encoding missing entries as -999. For categorical variable, encoding missing entries as string "unknown"

'mean_mode'(Mean/mode imputation): for numerial variable, impute the missing entries with the mean. For categorical variable, impute the missing entries with the mode

• drop_threshold: if 'deletion' strategy is selected, any column that have fraction of missing values exceed drop_threshold will be dropped. drop_threshold = 1 will keep all columns. Default drop_threshold = 0.7.

report.check_data_type()

Type conversion suggestions:

• String datetime -> datetime

• Small interger (for example: boolean) -> categorical type

• String float -> float

• Maximum cardinality (number of unique == number of observations) -> remove

>>> report.change_data_type()

Column Datetime converts to datetime

Exploratory type: numerical data

report.histogram()

The available parameters are:

• kde: boolean (default = False).

Exploratory type: categorical data

report.count_plots()

Exploratory type: text data

Development in progress...

Exploratory type: for numerical and numerical data

report.correlation()

Exploratory type: dimensionality reduction

report.pca()

Exploratory type: numerical and categorical data

report.boxplots()

Exploratory type: categorical and categorical data

report.cat_plots()

Exploratory type: categorical and categorical data

```python
report.correspondence_analysis()
```

Exploratory type: timeseries data

report.timeseries_plots(grouper = 'M')

The available parameters are:

• grouper: aggregate the timeseries with a time interval (default = 'W' for 1 week) using mean. This argument is used to reduce the datetime points we have to plot.

User can specify a target variable for classification/regression task using Classification And Regression Tree (CART).

1. Classification Report (train on 75% of data, test on 25%)

report.tree_model(max_depth = 4)

Classification Report on 25% of Testing Data:
              precision    recall  f1-score   support

 has disease       0.85      0.85      0.85        41
  no disease       0.83      0.83      0.83        35

    accuracy                           0.84        76
   macro avg       0.84      0.84      0.84        76
weighted avg       0.84      0.84      0.84        76

2. Bar chart of relative feature importance

3. Decision tree visualization with Dtreeviz