Index
A
- aggregation functions
- applying, to groups / Applying aggregation functions to groups
- Anaconda
- about / Getting Anaconda
- URL, for downloading / Getting Anaconda
- installing / Installing Anaconda
- installing, on Linux / Installing Anaconda on Linux
- installing, on Mac OS X / Installing Anaconda on Mac OS X
- installing, on Windows / Installing Anaconda on Windows
- anchored offsets
- application
- pandas, referencing / Referencing pandas in the application
- apply
- about / Apply
- aggregation functions, applying to groups / Applying aggregation functions to groups
- group data, transformation / The transformation of group data
- transformation / An overview of transformation
- transformation, examples / Practical examples of transformation
- groups, filtering / Filtering groups
- area plots
- about / Area plots
- array elements, NumPy
- selecting / Selecting array elements
B
- bar plots
- about / Bar plots
- binning
- about / Discretization and Binning
- box plots
- about / Box and whisker charts
C
- calendars
- used, for handling holidays / Handling holidays using calendars
- columns, DataFrame
- selecting / Selecting columns of a DataFrame
- renaming / Renaming columns
- adding / Adding and inserting columns
- inserting / Adding and inserting columns
- contents, replacing / Replacing the contents of a column
- deleting / Deleting columns in a DataFrame
- adding, via setting with enlargement / Adding rows (and columns) via setting with enlargement
- conda package manager
- using / Getting Anaconda
- used, for examining pandas version / Ensuring pandas is up to date
- contents, DataFrame
- modifying / Modifying the structure and content of DataFrame
- correlation / The correlation of stocks based on the daily percentage change of the closing price, Volatility calculation
- CSV
- about / CSV and Text/Tabular format
- URL / CSV and Text/Tabular format
- sample CSV data set / The sample CSV data set
- reading, onto DataFrame / Reading a CSV file into a DataFrame
- index column, specifyig on reading CSV file / Specifying the index column when reading a CSV file
- column names, specifying / Specifying column names
- specific columns to load, specifying / Specifying specific columns to load
- DataFrame, saving / Saving DataFrame to a CSV file
- CSV data
- loading, from files / Loading CSV data from files
- cumulative daily returns
- calculating / Calculating simple daily cumulative returns
D
- .drop() method / Removing rows using .drop()
- daily percentage change
- calculating / Calculating the simple daily percentage change
- data
- loading, from files / Loading data from files and the Web
- loading, from Web / Loading data from files and the Web, Loading data from the Web
- summorization / Summarized data and descriptive statistics
- writing, in Excel format / Reading and writing data in an Excel format
- reading, in Excel format / Reading and writing data in an Excel format
- accessing on Web, in cloud / Accessing data on the web and in the cloud
- reading, from remote data services / Reading data from remote data services
- stock data, reading from Yahoo! / Reading stock data from Yahoo! and Google Finance
- stock data, reading from Google Finance / Reading stock data from Yahoo! and Google Finance
- retrieving, from Yahoo! Finance Options / Retrieving data from Yahoo! Finance Options
- economic data, reading from Federal Reserve Bank of St. Louis / Reading economic data from the Federal Reserve Bank of St. Louis
- Kenneth French data, accessing / Accessing Kenneth French's data
- World Bank, reading from / Reading from the World Bank
- transforming / Transforming Data
- notebook, setting up / Setting up the IPython notebook
- concatenating / Concatenating data
- merging / Merging and joining data, An overview of merges
- joining / Merging and joining data
- merge operation, join semantics specifying / Specifying the join semantics of a merge operation
- pivoting / Pivoting
- stacking / Stacking and unstacking
- unstacking / Stacking and unstacking
- stacking, nonhierarchical indexes used / Stacking using nonhierarchical indexes
- unstacking, hierarchical indexes used / Unstacking using hierarchical indexes
- data, transforming
- mapping / Mapping
- values, replacing / Replacing values
- functions, applying for / Applying functions to transform data
- DataFrame
- creating, from scratch / Creating DataFrame from scratch
- example data / Example data
- columns, selecting / Selecting columns of a DataFrame
- rows selecting, index used / Selecting rows and values of a DataFrame using the index
- values selecting, index used / Selecting rows and values of a DataFrame using the index
- slicing, [] operator used / Slicing using the [] operator
- rows selecting, Boolean selection used / Selecting rows of a DataFrame by Boolean selection
- structure, modifying / Modifying the structure and content of DataFrame
- contents, modifying / Modifying the structure and content of DataFrame
- columns, renaming / Renaming columns
- columns, adding / Adding and inserting columns
- columns, inserting / Adding and inserting columns
- column contents, replacing / Replacing the contents of a column
- columns, deleting / Deleting columns in a DataFrame
- rows, adding / Adding rows to a DataFrame
- rows appending, .append() used / Appending rows with .append()
- objects concatenating, pd.concat() used / Concatenating DataFrame objects with pd.concat()
- rows adding, via setting with enlargement / Adding rows (and columns) via setting with enlargement
- columns adding, via setting with enlargement / Adding rows (and columns) via setting with enlargement
- rows, removing / Removing rows from a DataFrame
- rows removing, .drop() used / Removing rows using .drop()
- rows removing, Boolean selection used / Removing rows using Boolean selection
- rows removing, slice used / Removing rows using a slice
- scalar values, changing / Changing scalar values in a DataFrame
- arithmetic operations / Arithmetic on a DataFrame
- index, resetting / Resetting and reindexing
- index, reindexing / Resetting and reindexing
- CSV, reading into / Reading a CSV file into a DataFrame
- data type, inference / Data type inference and specification
- saving, to CSV / Saving DataFrame to a CSV file
- DataFrame, example data
- S&P 500 / S
- monthly stock historical prices / Monthly stock historical prices
- DataFrame object
- about / The pandas DataFrame object
- .iloc property / The pandas DataFrame object
- .loc property / The pandas DataFrame object
- data visualization
- date offsets
- about / Date offsets
- dates
- DatetimeIndex
- about / DatetimeIndex
- datetime object
- day
- density plot
- about / Density plot
- discretization
- about / Discretization and Binning
- duplicate data
- handling / Handling duplicate data
E
- economic data
- reading, from Federal Reserve Bank of St. Louis / Reading economic data from the Federal Reserve Bank of St. Louis
F
- Federal Reserve Economic Data (FRED) of St. Louis
- field-delimited data
- about / General field-delimited data
- noise rows, handling / Handling noise rows in field-delimited data
- files
- data, loading / Loading data from files and the Web
- CSV data, loading / Loading CSV data from files
- formatters
- used, for formatting axes tick date labels / Formatting axes tick date labels using formatters
G
- ggvis
- about / pandas and why it is important
- group data
- transformation / The transformation of group data
- grouping
- by single columns value / Grouping by a single column's values
- results, accessing / Accessing the results of grouping
- index levels used / Grouping using index levels
- groups
- aggregation functions, applying / Applying aggregation functions to groups
- filtering / Filtering groups, Discretization and Binning
H
- HDF5 format files
- reading / Reading and writing HDF5 format files
- heatmap
- hierarchical indexes
- used, for unstacking / Unstacking using hierarchical indexes
- hierarchical indexing
- about / Hierarchical indexing
- histograms
- about / Histograms
- holidays
- handling, calendars used / Handling holidays using calendars
- HTML data
- reading, from Web / Reading HTML data from the Web
I
- index labels
- used, for filling / Filling using index labels
- index levels
- used, for grouping / Grouping using index levels
- installation, Anaconda
- about / Installing Anaconda
- on Linux / Installing Anaconda on Linux
- on Mac OS X / Installing Anaconda on Mac OS X
- on Windows / Installing Anaconda on Windows
- installation, IPython Notebooks / Installing and running IPython Notebooks
- intervals
- IPython
- sample pandas application, executing / Running a small pandas sample in IPython
- IPython Notebooks
- about / pandas and IPython Notebooks, Starting the IPython Notebook server
- URL / pandas and IPython Notebooks, Starting the IPython Notebook server
- using / pandas and IPython Notebooks
- starting / Starting the IPython Notebook server
- installing / Installing and running IPython Notebooks
- executing / Installing and running IPython Notebooks
- examples / Installing and running IPython Notebooks
J
- JSON files
- reading / Reading and writing JSON files
- writing / Reading and writing JSON files
K
- Kenneth French data
- accessing / Accessing Kenneth French's data
- URL / Accessing Kenneth French's data
L
- Linux
- Anaconda, installing / Installing Anaconda on Linux
- logical operation
- on NumPy arrays / Logical operations on arrays
M
- Mac OS X
- Anaconda, installing / Installing Anaconda on Mac OS X
- markers
- reference link / Specifying line colors, styles, thickness, and markers
- mathematical operations
- NaN values, handling / How pandas handles NaN values in mathematical operations
- matplotlib
- about / pandas and why it is important
- melting
- about / Melting
- missing data
- working with / Working with missing data
- NaN values in Series, determining / Determining NaN values in Series and DataFrame objects
- DataFrame objects, determining / Determining NaN values in Series and DataFrame objects
- dropping / Selecting out or dropping missing data
- selecting out / Selecting out or dropping missing data
- filling in / Filling in missing data
- backward filling / Forward and backward filling of missing values
- forward filling / Forward and backward filling of missing values
- index labels, used for filling / Filling using index labels
- missing values, interpolation / Interpolation of missing values
- moving average calculation
- performing / Performing a moving-average calculation
- average daily returns, comparing against stocks / The comparison of average daily returns across stocks
- multiple plots, in single charts
- about / Multiple plots in a single chart
N
- NaN values
- determining, in Series / Determining NaN values in Series and DataFrame objects
- determining, in DataFrame objects / Determining NaN values in Series and DataFrame objects
- in mathematical operations / How pandas handles NaN values in mathematical operations
- nbviewer
- noise rows
- in field-delimited data, handling / Handling noise rows in field-delimited data
- nonhierarchical indexes
- used, for stacking / Stacking using nonhierarchical indexes
- Not-A-Number (NaN)
- notebook
- NumPy
- about / pandas and why it is important
- installing / Installing and importing NumPy
- importing / Installing and importing NumPy
- sliceability / Benefits and characteristics of NumPy arrays
- NumPy arrays
- advantages / Benefits and characteristics of NumPy arrays
- creating / Creating NumPy arrays and performing basic array operations
- operations, performing / Creating NumPy arrays and performing basic array operations
- elements, selecting / Selecting array elements
- logical operations / Logical operations on arrays
- slicing / Slicing arrays
- reshaping / Reshaping arrays
- combining / Combining arrays
- splitting / Splitting arrays
- numerical methods / Useful numerical methods of NumPy arrays
- NumPy ndarray / Alignment via index labels
O
- objects, DataFrame
- concatenating, pd.concat() used / Concatenating DataFrame objects with pd.concat()
- offsets
- used, for calculating new dates / Calculating new dates using offsets
- date offsets / Date offsets
- anchored offsets / Anchored offsets, Representing durations of time using Period objects
- period object / The Period object
- PeriodIndex / PeriodIndex
P
- .plot() method
- time-series charts, creating with / Creating time-series charts with .plot()
- pandas
- features / pandas and why it is important
- IPython Notebooks / pandas and IPython Notebooks
- referencing, in application / Referencing pandas in the application
- primary objects / Primary pandas objects
- version, examining with conda package manager / Ensuring pandas is up to date
- Wakari, using / Using Wakari for pandas
- importing / Importing pandas
- used, for plotting / Plotting basics with pandas
- PeriodIndex
- about / PeriodIndex
- period object
- about / The Period object
- plots, statistical analyses
- about / Common plots used in statistical analyses
- bar plots / Bar plots
- histograms / Histograms
- box plots / Box and whisker charts
- whisker charts / Box and whisker charts
- area plots / Area plots
- scatter plots / Scatter plots
- density plot / Density plot
- scatter plot matrix / The scatter plot matrix
- heatmap / Heatmaps
- primary objects
- about / Primary pandas objects
- Series object / The pandas Series object
- DataFrame object / The pandas DataFrame object
R
- remote data services
- data, reading from / Reading data from remote data services
- rows, DataFrame
- selecting, index used / Selecting rows and values of a DataFrame using the index
- selecting, by index label / Selecting rows by index label and location: .loc[] and .iloc[], Selecting rows by index label and/or location: .ix[]
- selecting, by location / Selecting rows by index label and location: .loc[] and .iloc[], Selecting rows by index label and/or location: .ix[]
- selecting, Boolean selection used / Selecting rows of a DataFrame by Boolean selection
- adding / Adding rows to a DataFrame
- adding, append() used / Appending rows with .append()
- adding, pd.concat() used / Concatenating DataFrame objects with pd.concat()
- adding, via setting with enlargement / Adding rows (and columns) via setting with enlargement
- removing / Removing rows from a DataFrame
- removing, .drop() used / Removing rows using .drop()
- removing, Boolean selection used / Removing rows using Boolean selection
- removing, slice used / Removing rows using a slice
S
- scalar lookup, DataFrame
- by label, .at[] used / Scalar lookup by label or location using .at[] and .iat[]
- by location, .at[] used / Scalar lookup by label or location using .at[] and .iat[]
- scalar values, DataFrame
- changing / Changing scalar values in a DataFrame
- scatter plot
- about / Scatter plots
- scatter plot matrix
- about / The scatter plot matrix
- scikit-learn
- about / pandas and why it is important
- SciPy
- about / pandas and why it is important
- Series object
- about / The pandas Series object, The Series object
- creating / Creating Series
- items, determining / Size, shape, uniqueness, and counts of values
- .size property, using / Size, shape, uniqueness, and counts of values
- .shape property, using / Size, shape, uniqueness, and counts of values
- .count() method, using / Size, shape, uniqueness, and counts of values
- .unique() method, using / Size, shape, uniqueness, and counts of values
- .value_counts(), using / Size, shape, uniqueness, and counts of values
- .head() method, using / Peeking at data with heads, tails, and take
- .tail() method, using / Peeking at data with heads, tails, and take
- .take() method, using / Peeking at data with heads, tails, and take
- values, looking up / Looking up values in Series
- alignment, examining via index labels / Alignment via index labels
- arithmetic operations / Arithmetic operations
- Boolean selection / Boolean selection
- reindexing / Reindexing a Series
- modifying, in-place / Modifying a Series in-place
- slicing / Slicing a Series
- slicing, DataFrame
- [] operator used / Slicing using the [] operator
- split
- notebook, setting up / Setting up the IPython notebook
- aggregation / The split, apply, and combine (SAC) pattern
- transformation / The split, apply, and combine (SAC) pattern
- filtration / The split, apply, and combine (SAC) pattern
- URL / The split, apply, and combine (SAC) pattern
- about / Split
- examples, data / Data for the examples
- grouping, by single columns values / Grouping by a single column's values
- grouping results, accessing / Accessing the results of grouping
- grouping, index levels used / Grouping using index levels
- split-apply-combine (SAC) pattern
- SQL databases
- reading, from / Reading and writing from/to SQL databases
- writing to / Reading and writing from/to SQL databases
- SQLite Data Browser
- stacked data
- stacking
- about / Stacking and unstacking
- nonhierarchical indexes used / Stacking using nonhierarchical indexes
- hierarchical indexes used / Unstacking using hierarchical indexes
- statistics
- stock data
- reading, from Yahoo! / Reading stock data from Yahoo! and Google Finance
- reading, from Google Finance / Reading stock data from Yahoo! and Google Finance
- notebook, setting up / Setting up the IPython notebook
- from Yahoo!, obtaining / Obtaining and organizing stock data from Yahoo!
- from Yahoo!, organizing / Obtaining and organizing stock data from Yahoo!
- obtaining, from Yahoo! / Obtaining and organizing stock data from Yahoo!
- resampling, from from daily to monthly returns / Resampling data from daily to monthly returns
- moving average calculation, performing / Performing a moving-average calculation
- and average daily returns, comparing / The comparison of average daily returns across stocks
- correlating / The correlation of stocks based on the daily percentage change of the closing price
- volatility / Volatility calculation
- risk relative to expected returns, determining / Determining risk relative to expected returns
- structure, DataFrame
- modifying / Modifying the structure and content of DataFrame
T
- tidy data
- about / What is tidying your data?
- URL / What is tidying your data?
- time
- time-series charts
- creating, with .plot() method / Creating time-series charts with .plot()
- time-series data
- about / Introducing time-series data
- DatetimeIndex / DatetimeIndex
- creating, with specific frequencies / Creating time-series data with specific frequencies
- manipulating / Manipulating time-series data
- lagging / Shifting and lagging
- shifting / Shifting and lagging
- frequency conversion / Frequency conversion
- resampling / Up and down resampling
- moving window operations / Time-series moving-window operations
- time-series plot
- adorning / Adorning and styling your time-series plot
- styling / Adorning and styling your time-series plot
- title, adding / Adding a title and changing axes labels
- axes labels, modifying / Adding a title and changing axes labels
- legend content, specifying / Specifying the legend content and position
- legend position, specifying / Specifying the legend content and position
- line colors, specifying / Specifying line colors, styles, thickness, and markers
- styles, specifying / Specifying line colors, styles, thickness, and markers
- thickness, specifying / Specifying line colors, styles, thickness, and markers
- markers, specifying / Specifying line colors, styles, thickness, and markers
- tick mark locations, specifying / Specifying tick mark locations and tick labels
- tick labels, specifying / Specifying tick mark locations and tick labels
- axes tick date labels, formatting with formatters / Formatting axes tick date labels using formatters
- reference link / Formatting axes tick date labels using formatters
- time-series prices
- plotting / Plotting time-series prices
- volume series data, plotting / Plotting volume-series data
- simple daily percentage change, calculating / Calculating the simple daily percentage change
- cumulative daily return, calculating / Calculating simple daily cumulative returns
- data, resampling from daily to monthly returns / Resampling data from daily to monthly returns
- distribution of returns, analyzing / Analyzing distribution of returns
- Timedelta
- about / Timedelta
- time objects
- timestamp objects
- about / Timestamp objects
- timestamps
- normalizing, time zones used / Normalizing timestamps using time zones
- time zones
- used, for normalizing timestamps / Normalizing timestamps using time zones
- transformation
- of group data / The transformation of group data
- about / An overview of transformation
- examples / Practical examples of transformation
U
- unstacking
- about / Stacking and unstacking
V
- values, DataFrame
- selecting, index used / Selecting rows and values of a DataFrame using the index
- volume series data
- plotting / Plotting volume-series data
W
- Wakari
- URL / Installing and running IPython Notebooks, Using Wakari for pandas
- using, for pandas / Using Wakari for pandas
- URL, for examples / Using Wakari for pandas
- Web
- data, loading / Loading data from files and the Web, Loading data from the Web
- whisker charts
- about / Box and whisker charts
- Windows
- Anaconda, installing / Installing Anaconda on Windows
- World Bank
- data, reading from / Reading from the World Bank
- URL / Reading from the World Bank
Y
- Yahoo!
- stock data, obtaining / Obtaining and organizing stock data from Yahoo!
- stock data, organizing / Obtaining and organizing stock data from Yahoo!
- Yahoo! Finance Options
- data, retrieving from / Retrieving data from Yahoo! Finance Options