Package: quickda
Quick-EDASimple & Easy-to-use python modules to perform Quick Exploratory Data Analysis for any structured dataset!Getting StartedYou will need to havePython 3andJupyter Notebookinstalled in your local system. Once installed, clone this repository to your local to get the project structure setup.git clone https://github.com/sid-the-coder/QuickDA.gitYou will also need to install few python package dependencies in your evironment to get started. You can do this by:pip3 install -r requirements.txtOR you can also install the package fromPyPi Indexusing the pip installer:pip3 install quickdaTable of ContentsData Exploration - explore(data)data: pd.DataFramemethod: string, default="summarize""summarize" : Generates a summary statistics of the dataset"profile" : Generates a HTML Report of the Dataset Profilereport_name: string, default="Dataset Report"Parameter to customise the generated report nameis_large_dataset: Boolean, default=FalseParameter set to True explicitly to flag, in case of a large datasetData Cleaning - clean(data): [Returns DataFrame]data: pd.DataFramemethod: 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 methodcolumns: list/string, default=[]Parameter to specify column names in the DataFramedtype: string, default="numeric""numeric" : Converts columns dtype to numeric"category" : Converts columns dtype to category"datetime" : Converts columns dtype to datetimeto_replace: string/integer/regex, default=""Parameter to pass a value to replace in the DataFranevalue: string/integer/regex, default=np.nanParameter to pass a new value that replaces an old value in the Dataframethreshold: float, default=0Parameter to set threshold in the range of [0,1] for cardinalityEDA Numerical Features - eda_num(data)data: pd.DataFramemethod: string, default="default""default" : Shows all Outlier & Distribution Analysis via BoxPlots & Histograms"correlation" : Gets the correlation matrix between all numerical featuresbins: integer, default=10Parameter to set the number of bins while displaying histogramsEDA Categorical Features - eda_cat(data, x)data: pd.DataFramex: string, First Categorical Type Column Namey: string, default=NoneParameter to pass the Second Categorical Type Column Namemethod: string, default="default""default" : Shows category count plot & summarizes it in a frequency tableEDA Numerical with Categorical Features - eda_numcat(data, x, y)data: pd.DataFramex: 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 functionhue: string, default=NoneParameter to visualise a categorical Type feature within scatterplotsvalues: string/list, default=NoneParameter to set columns to aggregate on pivot viewsaggfunc: string, default="mean"Parameter to set aggregate functions on pivot tablesExample: 'min', 'max', 'mean', 'median', 'sum', 'count'EDA Time Series Data - eda_timeseries(data, x, y)data: pd.DataFramex: string, Datetime Type Column Namey: string, Numeric Type Column NameUpcoming WorkBasic Preprocessing for Text Data- Tokenization, Normalization, Noise Removal, LemmatizationEDA for Text Data- NGrams, POS tagging, Word Cloud, Sentiment AnalysisQuick Insight Generation for all EDA steps- Generate easy-to-read textual insights