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Essential Python libraries for Data Science

Photo by Myriam Jessier on Unsplash

1. Pandas

  1. Series (1D): The series is a one-dimensional data structure. It can be considered as a 1D labeled array that is capable of holding data of any type. For example, a column of a table.
  2. Dataframe (2D): It is a two-dimensional data structure. It can be considered as a 2D labeled array. For example, a table with both rows and columns.
  1. Pandas documentation: Click here
  2. Pandas cheat sheet: Click here

2. NumPy

  1. Numpy documentation: Click here
  2. Numpy cheat sheet: Click here

3. SciPy

  1. SciPy documentation: Click here
  2. SciPy cheat sheet: Click here

4. Matplotlib

  1. Line plots
  2. Bar charts and histograms
  3. Scatter plots
  4. Area plots
  5. Pie charts
  6. Contour plots
  7. Stem plots
  8. Quiver plots
  9. Spectrograms
  10. Stream Plots
  1. Matplotlib documentation: Click here
  2. Matplotlib cheat sheet: Click here

5. Seaborn

  1. Seaborn documentation: Click here
  2. Seaborn cheat sheet: Click here

6. Plotly

  1. Plotly documentation: Click here
  2. Plotly cheat sheet: Click here

7. Scikit Learn

  1. Classification
  2. Regression
  3. Clustering
  4. Dimensionality Reduction
  5. Model Selection
  6. Data Preprocessing
  1. Scikit Learn documentation: Click here
  2. Scikit Learn cheat sheet: Click here

8. TensorFlow

  1. TensorFlow documentation: Click here

9. Keras

  1. Keras documentation: Click here
  2. Keras cheat sheet: Click here

10. Statsmodels

  1. Statsmodels documentation: Click here

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