Toggle navigation
JUPYTER
FAQ
View as Code
View on GitHub
Execute on Binder
Download Notebook
tutorials
pandas
Notebook
pandas Dataframe - Basic Operativity
1 File I/O and DataFrame Generation
1.1 Create DataFrames with read_csv
1.2 Create DataFrames from Python Dictionaries
1.3 Create DataFrames from Items
1.4 Create DataFrames fron Numpy Arrays
1.5 DataFrames can be converted in Numpy Arrays
1.6 DataFrames, Series and Panels
2 Automatic Data Alignment
3 Indexing
3.1 Label-Based Indexing
3.2 Position-Based Indexing
3.3 Advanced Indexing - .ix
4 DataFrame Basic Operations
4.1 Reindex/Reorder rows and columns
4.2 Calculate new columns
4.3 Deleting rows and columns
4.4 Inserting colums in a specific position
4.5 Check if a value or a list of given values are contained in a specific column
4.6 Rename columns
4.7 Iterate efficiently through rows
5 Duplicated Data
5.1 Find duplicated data in columns
5.2 Remove Duplicates
6 Working with Large Arrays
6.1 Control the DataFrame memory occupation
6.2 Explore large arrays
7 Column pct_change and shift
8 Reindex
9 More on Indexing: Multi Index
10 Package Options
pandas I/O tools and examples
1 Matlab Variables
1.1 Import a Matlab variable from file
2 Importing a compressed CSV
3 Importing and visualizing geographical data
4 Importing JSON files
5 Importing HTML
6 Importing Excel
7 Working with SQL and databases
7.1 Write SQL
7.2 Import SQL
8 Working with HDF5
8.1 Storer format
8.2 Table format
8.3 Querying a Table
Pandas Time series
1 Timestamps and DatetimeIndex
2 DateOffsets objects
3 Indexing with a DateTime index
4 Frequency conversion
5 Filling gaps
Statistical tools
1 Percent change
2 Covariance
3 Correlation
4 Rolling moments and Binary rolling moments
5 A pratical example: Return indexes and cumulative returns
Merge and pivot
1 Concat
2 Append
3 Join
4 Merge
5 Pivoting
6 Stack and Unstack
Split apply and combine
1 Groupby
2 Aggregate
3 Apply
4 A pratical example: Normalize by year
5 A practical example: Group and standardize by dimension
Sources of Open Data
1 Yahoo! Finance
1.1 Plotting timeseries with bokeh:
1.2 Plotting candlesticks with bokeh:
1.3 Plotting data ranges with bokeh:
1.4 Plotting multiple plots with matplotlib:
2 Google Finance
3 Federal Reserve Economic Data
4 World Bank
Baby Names
1 Load and prepare the data
2 Pivoting
3 Splitting
4 Using 'groupby'