Analytics

Using pandas, matplotlib and seaborn libraries in data analytics in the different jupyter notebooks in the repository. This readme is a table of contents of the notebooks.

Data Manipulation with Pandas

1. Aggregating data frames

• summary statistics: mean, median, cumulative stats
• the .agg( ) method
• dropping duplicates
• counting categorical variables
• calculating after using .groupby()
• multiple grouped summaries
• pivot tables
• filling in missing values

2. Creating and Visualizing dataframes

• bar plots, line plots, scatter plots, histograms
• list of dictionaries to a dataframe
• dictionary of lists to a DataFrame
• reading and writing CSVs using pandas

3. Data manipulation using pandas

• components of a dataframe
• sorting dataframe columns
• subsetting columns
• subsetting rows
• subsetting rows by categorical variables
• adding new columns to a DataFrame

4. Slicing and indexing dataframes

• setting and removing indexes
• subsetting with .loc[ ]
• setting multi level indexes
• sorting by index values
• slicing and subsetting with .loc( ) and iloc( )
• slicing time series
• subsetting by rows / columns
• creating pivot tables
• subsetting pivot tables
• calculating in pivot tables

Joining data with pandas

1. Data merging basics

• inner joins
• relationships
• one to many merge

2. Merging tables with different merge types

• left join
• right join
• outer join
• self join
• merging on indexes (single index, multi-index, left_on, right_on)

3. Merging ordered or time series data

• merge_ordered( )
• foward filling technique
• merge_asof( )
• selecting with .query( )
• reshaping data with melt

Data Visualization with Matplotlib

1. Introduction to Matplotlib

• adding data to the axes object
• customizing plots (adding markers, setting linestyle, choosing color, adding axes labels, adding titles)
• subplots
• creating small multiples with plt.subplots( )
• shared y axis

2. Plotting Time series data

• read data with time index
• plotting time series with different variables
• annotating time series data

3. Quantitative comparisons and statistical visualizations

• quantitative comparisons with barcharts
• stacked bar charts
• quantitative comparisons with histograms
• statistical plotting - errorbars
• boxplots
• quantitative comparisons with scatter plots

4. Sharing Visualizations

• plotting styles
• saving visualizations
• automating figures from data

Data Visualization with seaborn

1. Introduction to seaborn

• introduction
• scatter plots
• count plots
• adding a third variable using hue
• setting hue order
• specifying hue colors

2. Visualizing Quantitative variables

• introduction to relational plots (enables you to visualize the relationship between two quantitative variables using either scatter plots or line plots.)and subplots
• scatterplot() vs. relplot()
• subplots in columns / rows / both
• wrapping cols
• ordering cols
• two factor subplots
• customizing Scatter Plots (size, point style, transparency)
• line plots

3. Visualizing Categorical and quantitative variables

• countplots and barplots
• boxplots
• pointplots

4. Customizing seaborn plots

• changing plot style and color
• changing the figure style
• changing the palette (Diverging, Sequential)
• changing the scale
• adding Titles and Labels