Data Cleaning, Analysis and Preparation takes a huge chunk of any Data Science project pipeline. And there a lot of libraries in Python to get the job done, the most popular being - Pandas. 
However, QuickDA is another low-code library which offers data exploration, cleaning and visualization in very few lines of code! Let’s go over some of its features:
Exploring the dataset: The explore() function quickly tells you about the features present, missing values, correlations, cardinality, distribution of data, skewness and much more. It even has Pandas profiling report inbuilt which gives an easy overview.
Cleaning: The clean() function offers multiple methods to clean the data such as: standardizing columns names, dropping useless features and duplicate rows, imputing missing values, reducing cardinality, modifying column data types, removing outliers, and much more.
EDA: The eda_num() function lets you visualize the numerical data with multiple plots such as histograms and correlation matrix. For categorical features, QuickDA has the eda_cat() function. It also has the methods for plotting predictive Power Score matrix, scatter plots, violin plots and even time series data, using the eda_timeseries() function.
#datascience #python #machinelearning