Here are some pyhton code snippets that helps you code in just a few lines of code

sidetable.ipynb-Sidetable creates a frequency distribution table,missing value table, and their sums based on selected columns.Consider we have a dataset that contains some measurements on a categorical variable (e.g. model). We have many different models and each model has many observations (rows). By using sidetable, we get an overview that shows how much each model occupies in the dataset. This can also be achieved using value_counts function of pandas but sidetable is more informative as we will see in the examples.

Pandas_Profiling.ipynb-Pandas_Profiling does a quick analysis of your data with just one line of code. It tells you the number of missing values, which variables are categorical, numerical,boolen..etc, finds their cardinality, and even finds the correlation of the numerical variables at ease.

categorical.ipynb-Extracting categorical variables and numerical variables from the dataset can be done simply by just using one line of code.

Quandle.ipynb-Quandl unifies financial and economic datasets from hundreds of publishers on a single user-friendly platform.Most datasets on Quandl are available directly in Python, using the Quandl Python module.Here I have obtained the closing prices stock of apple, Facebook, Walmart, Google, and Tesla for my desired dates.

Multiple_Regreesion.ipynb-1. Seaborn charts to visualize the variable distributions to check if they are normally distributed 2. Quantiles to reduce the outliers 3. Converting exponential distribution to linear using log transformation 4. VIF to check multi-collinearity