Great resources for learning data science with Python including tutorials, code snippets, blog pieces, and lectures, as well as libraries.

• General Jupyter Tricks
• Fix Jupyter Notebook
• Python Debugger (PDB)
  • Blog Post
  • Video
  • Cheatsheet
• cookiecutter-data-science - Data Science Project Templates.
• nteract - Open Jupyter Notebooks with doubleclick.
• papermill - Parameterize and execute Jupyter notebooks(tutorial).
• nbdime - Different two notebook files, Alternative GitHub App: ReviewNB.
• RISE - Turn Jupyter notebooks files into presentations.
• qgrid - Pandas DataFrame sorting.
• pivottablejs - Drag n drop Pivot Tables and Charts for jupyter notebooks.
• itables - Interactive tables in Jupyter.
• jupyter-datatables - Interactive tables in Jupyter.
• debugger - Visual debugger for Jupyter.
• nbcommands - View and search notebooks from terminal.
• handcalcs - More convenient way of writing mathematical equations in Jupyter.

• NumPy - Multidimensional Arrays
• Pandas - Data structures built on top of NumPy library
• pandas_summary - Basic statistics using DataFrameSummary(df).summary().
• pandas_profiling - Descriptive statistics using ProfileReport.
• Matplotlib - Visualization library.
• Seaborn - Data visualization library based on matplotlib.
• scikit-learn - Core Machine Learning library.
• sklearn_pandas - Helpful DataFrameMapper class.
• missingno - Missing data visualization.
• rainbow-csv - Plugin to display .csv files with nice colors.

• Top 5 Best Pandas Tricks
• df.pipe - Function to improve code readability
• pandasvault - Large collection of pandas tricks.
• vaex - Out-of-Core DataFrames.
• modin - Parallelization library for faster pandas DataFrame.
• xarray - Extends pandas to n-dimensional arrays.
• pandarallel - Parallelize pandas operations.
• pandapy - Additional features for pandas.
• pandas-log - Find business logic issues and performance issues in pandas.
• pandas_flavor - Write custom accessors like .str and .dt.
• swifter - Apply any function to a pandas dataframe faster.

• tqdm - Progress bars for for-loops. Also supports pandas apply().
• icecream - Simple debugging output.
• pyprojroot - Helpful here() command from R.
• intake - Loading datasets made easier, talk.
• loguru - Python logging.

• camelot - Extract text from PDF.
• textract - Extract text from any document.

• spark - DataFrame for big data
  • cheatsheet
  • tutorial.
• sparkit-learn
  • spark-deep-learning - ML frameworks for spark.
• koalas - Pandas API on Apache Spark.
• dask
  • dask-ml - Pandas DataFrame for big data and machine learning library
  • resources, talk1
  • talk2
  • notebooks
  • videos.
  • dask-gateway - Managing dask clusters.

• ni - Command line tool for big data.
• xsv - Command line tool for indexing, slicing, analyzing, splitting and joining CSV files.
• csvkit - Another command line tool for CSV files.
• csvsort - Sort large csv files.
• tsv-utils - Tools for working with CSV files by ebay.
• cheat - Make cheatsheets for command line commands.

• scipy.stats - Statistical tests.
• pingouin - Statistical tests.
• researchpy - Helpful summary_cont() function for summary statistics (Table 1).
• scikit-posthocs - Statistical post-hoc tests for pairwise multiple comparisons.
• Bland-Altman Plot - Plot for agreement between two methods of measurement.
• ANOVA
  • Tutorials: One-way
  • Two-way
  • Type 1,2,3 explained.

• Correlation
• Null Hypothesis Significance Testing (NHST) and Sample Size Calculation
• Cohen's d
• Confidence Interval
• Equivalence, non-inferiority and superiority testing
• Bayesian two-sample t test
• Distribution of p-values when comparing two groups
• Understanding the t-distribution and its normal approximation

• Inverse Propensity Weighting
• Dealing with Selection Bias By Propensity Based Feature Selection
• Talk

• Greenland - Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
• Lindeløv - Common statistical tests are linear models
• Chatruc - The Central Limit Theorem and its misuse
• Al-Saleh - Properties of the Standard Deviation that are Rarely Mentioned in Classrooms
• Cook - Estimating the chances of something that hasn’t happened yet
• Wainer - The Most Dangerous Equation
• Gigerenzer - The Bias Bias in Behavioral Economics

• scikit-learn - General machine learning framework.
• h2o - Machine learning framework.
• caffe - Deep learning framework
  • pretrained models.
• mxnet - Deep learning framework
  • book.

• pyemd - Earth Mover's Distance, similarity between histograms.
• Kaggler - Utility functions (OneHotEncoder(min_obs=100))
• tspreprocess - Time series preprocessing: Denoising, Compression, Resampling.
• imbalanced-learn - Resampling for imbalanced datasets.
• fancyimpute - Matrix completion and imputation algorithms.
• impyute - Imputations.
• janitor - Clean messy column names.
• littleballoffur - Sampling from graphs.
• Checklist.

• iterative-stratification - Stratification of multilabel data.

• sklearn - Pipeline
  • Examples.
• pdpipe - Pipelines for DataFrames.
• scikit-lego - Custom transformers for pipelines.
• skoot - Pipeline helper functions.
• categorical-encoding - Categorical encoding of variables
  • vtreat (R package).
• dirty_cat - Encoding dirty categorical variables.
• patsy - R-like syntax for statistical models.
• mlxtend - LDA.
• featuretools - Automated feature engineering
  • Example.
• feature_engine - Encoders, transformers
• pypeln - Concurrent data pipelines.
• tsfresh - Time series feature engineering.

• Talk
• Blog post series -
  • 1
  • 2
  • 3
  • 4
• Tutorials
  • 1
  • 2
• sklearn - Feature selection.
• stability-selection - Stability selection.
• scikit-rebate - Relief-based feature selection algorithms.
• scikit-genetic - Genetic feature selection.
• boruta_py - Feature selection
  • explaination,
  • example.
• linselect - Feature selection package.
• mlxtend - Exhaustive feature selection.
• BoostARoota - Xgboost feature selection algorithm.
• INVASE - Instance-wise Variable Selection using Neural Networks.