{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "* [__pandas Dataframe - Basic Operativity__](pd01v01_basic_data_operativity.ipynb)\n", " * [1 File I/O and DataFrame Generation](pd01v01_basic_data_operativity.ipynb#1-File-I/O-and-DataFrame-Generation)\n", " * [1.1 Create DataFrames with read_csv](pd01v01_basic_data_operativity.ipynb#1.1-Create-DataFrames-with-read_csv)\n", " * [1.2 Create DataFrames from Python Dictionaries](pd01v01_basic_data_operativity.ipynb#1.2-Create-DataFrames-from-Python-Dictionaries)\n", " * [1.3 Create DataFrames from Items](pd01v01_basic_data_operativity.ipynb#1.3-Create-DataFrames-from-Items)\n", " * [1.4 Create DataFrames fron Numpy Arrays](pd01v01_basic_data_operativity.ipynb#1.4-Create-DataFrames-fron-Numpy-Arrays)\n", " * [1.5 DataFrames can be converted in Numpy Arrays](pd01v01_basic_data_operativity.ipynb#1.5-DataFrames-can-be-converted-in-Numpy-Arrays)\n", " * [1.6 DataFrames, Series and Panels](pd01v01_basic_data_operativity.ipynb#1.6-DataFrames,-Series-and-Panels)\n", " * [2 Automatic Data Alignment](pd01v01_basic_data_operativity.ipynb#2-Automatic-Data-Alignment)\n", " * [3 Indexing](pd01v01_basic_data_operativity.ipynb#3-Indexing)\n", " * [3.1 Label-Based Indexing](pd01v01_basic_data_operativity.ipynb#3.1-Label-Based-Indexing)\n", " * [3.2 Position-Based Indexing](pd01v01_basic_data_operativity.ipynb#3.2-Position-Based-Indexing)\n", " * [3.3 Advanced Indexing - .ix](pd01v01_basic_data_operativity.ipynb#3.3-Advanced-Indexing---.ix)\n", " * [4 DataFrame Basic Operations](pd01v01_basic_data_operativity.ipynb#4-DataFrame-Basic-Operations)\n", " * [4.1 Reindex/Reorder rows and columns](pd01v01_basic_data_operativity.ipynb#4.1-Reindex/Reorder-rows-and-columns)\n", " * [4.2 Calculate new columns](pd01v01_basic_data_operativity.ipynb#4.2-Calculate-new-columns)\n", " * [4.3 Deleting rows and columns](pd01v01_basic_data_operativity.ipynb#4.3-Deleting-rows-and-columns)\n", " * [4.4 Inserting colums in a specific position](pd01v01_basic_data_operativity.ipynb#4.4-Inserting-colums-in-a-specific-position)\n", " * [4.5 Check if a value or a list of given values are contained in a specific column](pd01v01_basic_data_operativity.ipynb#4.5-Check-if-a-value-or-a-list-of-given-values-are-contained-in-a-specific-column)\n", " * [4.6 Rename columns](pd01v01_basic_data_operativity.ipynb#4.6-Rename-columns)\n", " * [4.7 Iterate efficiently through rows](pd01v01_basic_data_operativity.ipynb#4.7-Iterate-efficiently-through-rows)\n", " * [5 Duplicated Data](pd01v01_basic_data_operativity.ipynb#5-Duplicated-Data)\n", " * [5.1 Find duplicated data in columns](pd01v01_basic_data_operativity.ipynb#5.1-Find-duplicated-data-in-columns)\n", " * [5.2 Remove Duplicates](pd01v01_basic_data_operativity.ipynb#5.2-Remove-Duplicates)\n", " * [6 Working with Large Arrays](pd01v01_basic_data_operativity.ipynb#6-Working-with-Large-Arrays)\n", " * [6.1 Control the DataFrame memory occupation](pd01v01_basic_data_operativity.ipynb#6.1-Control-the-DataFrame-memory-occupation)\n", " * [6.2 Explore large arrays](pd01v01_basic_data_operativity.ipynb#6.2-Explore-large-arrays)\n", " * [7 Column pct_change and shift](pd01v01_basic_data_operativity.ipynb#7-Column-pct_change-and-shift)\n", " * [8 Reindex](pd01v01_basic_data_operativity.ipynb#8-Reindex)\n", " * [9 More on Indexing: Multi Index](pd01v01_basic_data_operativity.ipynb#9-More-on-Indexing:-Multi-Index)\n", " * [10 Package Options](pd01v01_basic_data_operativity.ipynb#10-Package-Options)\n", "* [__pandas I/O tools and examples__](pd02v01_input_output.ipynb)\n", " * [1 Matlab Variables](pd02v01_input_output.ipynb#1-Matlab-Variables)\n", " * [1.1 Import a Matlab variable from file](pd02v01_input_output.ipynb#1.1-Import-a-Matlab-variable-from-file)\n", " * [2 Importing a compressed CSV](pd02v01_input_output.ipynb#2-Importing-a-compressed-CSV)\n", " * [3 Importing and visualizing geographical data](pd02v01_input_output.ipynb#3-Importing-and-visualizing-geographical-data)\n", " * [4 Importing JSON files](pd02v01_input_output.ipynb#4-Importing-JSON-files)\n", " * [5 Importing HTML](pd02v01_input_output.ipynb#5-Importing-HTML)\n", " * [6 Importing Excel](pd02v01_input_output.ipynb#6-Importing-Excel)\n", " * [7 Working with SQL and databases](pd02v01_input_output.ipynb#7-Working-with-SQL-and-databases)\n", " * [7.1 Write SQL](pd02v01_input_output.ipynb#7.1-Write-SQL)\n", " * [7.2 Import SQL](pd02v01_input_output.ipynb#7.2-Import-SQL)\n", " * [8 Working with HDF5](pd02v01_input_output.ipynb#8-Working-with-HDF5)\n", " * [8.1 Storer format](pd02v01_input_output.ipynb#8.1-Storer-format)\n", " * [8.2 Table format](pd02v01_input_output.ipynb#8.2-Table-format)\n", " * [8.3 Querying a Table](pd02v01_input_output.ipynb#8.3-Querying-a-Table)\n", "* [__Pandas Time series__](pd03v01_time_series.ipynb)\n", " * [1 Timestamps and DatetimeIndex](pd03v01_time_series.ipynb#1-Timestamps-and-DatetimeIndex)\n", " * [2 DateOffsets objects](pd03v01_time_series.ipynb#2-DateOffsets-objects)\n", " * [3 Indexing with a DateTime index](pd03v01_time_series.ipynb#3-Indexing-with-a-DateTime-index)\n", " * [4 Frequency conversion](pd03v01_time_series.ipynb#4-Frequency-conversion)\n", " * [5 Filling gaps](pd03v01_time_series.ipynb#5-Filling-gaps)\n", "* [__Statistical tools__](pd04v01_statistical_tools.ipynb)\n", " * [1 Percent change](pd04v01_statistical_tools.ipynb#1-Percent-change)\n", " * [2 Covariance](pd04v01_statistical_tools.ipynb#2-Covariance)\n", " * [3 Correlation](pd04v01_statistical_tools.ipynb#3-Correlation)\n", " * [4 Rolling moments and Binary rolling moments](pd04v01_statistical_tools.ipynb#4-Rolling-moments-and-Binary-rolling-moments)\n", " * [5 A pratical example: Return indexes and cumulative returns](pd04v01_statistical_tools.ipynb#5-A-pratical-example:-Return-indexes-and-cumulative-returns)\n", "* [__Merge and pivot__](pd05v01_data_organization.ipynb)\n", " * [1 Concat](pd05v01_data_organization.ipynb#1-Concat)\n", " * [2 Append](pd05v01_data_organization.ipynb#2-Append)\n", " * [3 Join](pd05v01_data_organization.ipynb#3-Join)\n", " * [4 Merge](pd05v01_data_organization.ipynb#4-Merge)\n", " * [5 Pivoting](pd05v01_data_organization.ipynb#5-Pivoting)\n", " * [6 Stack and Unstack](pd05v01_data_organization.ipynb#6-Stack-and-Unstack)\n", "* [__Split apply and combine__](pd06v01_advanced_data_management.ipynb)\n", " * [1 Groupby](pd06v01_advanced_data_management.ipynb#1-Groupby)\n", " * [2 Aggregate](pd06v01_advanced_data_management.ipynb#2-Aggregate)\n", " * [3 Apply](pd06v01_advanced_data_management.ipynb#3-Apply)\n", " * [4 A pratical example: Normalize by year](pd06v01_advanced_data_management.ipynb#4-A-pratical-example:-Normalize-by-year)\n", " * [5 A practical example: Group and standardize by dimension](pd06v01_advanced_data_management.ipynb#5-A-practical-example:-Group-and-standardize-by-dimension)\n", "* [__Sources of Open Data__](pd07v01_open_data.ipynb)\n", " * [1 Yahoo! Finance](pd07v01_open_data.ipynb#1-Yahoo!-Finance)\n", " * [1.1 Plotting timeseries with bokeh:](pd07v01_open_data.ipynb#1.1-Plotting-timeseries-with-bokeh:)\n", " * [1.2 Plotting candlesticks with bokeh:](pd07v01_open_data.ipynb#1.2-Plotting-candlesticks-with-bokeh:)\n", " * [1.3 Plotting data ranges with bokeh:](pd07v01_open_data.ipynb#1.3-Plotting-data-ranges-with-bokeh:)\n", " * [1.4 Plotting multiple plots with matplotlib:](pd07v01_open_data.ipynb#1.4-Plotting-multiple-plots-with-matplotlib:)\n", " * [2 Google Finance](pd07v01_open_data.ipynb#2-Google-Finance)\n", " * [3 Federal Reserve Economic Data](pd07v01_open_data.ipynb#3-Federal-Reserve-Economic-Data)\n", " * [4 World Bank](pd07v01_open_data.ipynb#4-World-Bank)\n", "* [__Baby Names__](pd08v01_babynames.ipynb)\n", " * [1 Load and prepare the data](pd08v01_babynames.ipynb#1-Load-and-prepare-the-data)\n", " * [2 Pivoting](pd08v01_babynames.ipynb#2-Pivoting)\n", " * [3 Splitting](pd08v01_babynames.ipynb#3-Splitting)\n", " * [4 Using 'groupby'](pd08v01_babynames.ipynb#4-Using-'groupby')\n" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }