Categories can be removed by using the .remove_categories() method. Values that are removed are replaced by np.NaN. The following screenshot demonstrates this by removing the bronze category:
- Preface
- pandas and Data Analysis
- Introducing pandas
- Data manipulation, analysis, science, and pandas
- The process of data analysis
- Relating the book to the process
- Concepts of data and analysis in our tour of pandas
- Other Python libraries of value with pandas
- Summary
- Up and Running with pandas
- Representing Univariate Data with the Series
- Configuring pandas
- Creating a Series
- The .index and .values properties
- The size and shape of a Series
- Specifying an index at creation
- Heads, tails, and takes
- Retrieving values in a Series by label or position
- Slicing a Series into subsets
- Alignment via index labels
- Performing Boolean selection
- Re-indexing a Series
- Modifying a Series in-place
- Summary
- Representing Tabular and Multivariate Data with the DataFrame
- Manipulating DataFrame Structure
- Configuring pandas
- Renaming columns
- Adding new columns with [] and .insert()
- Adding columns through enlargement
- Adding columns using concatenation
- Reordering columns
- Replacing the contents of a column
- Deleting columns
- Appending new rows
- Concatenating rows
- Adding and replacing rows via enlargement
- Removing rows using .drop()
- Removing rows using Boolean selection
- Removing rows using a slice
- Summary
- Indexing Data
- Configuring pandas
- The importance of indexes
- The pandas index types
- The fundamental type - Index
- Integer index labels using Int64Index and RangeIndex
- Floating-point labels using Float64Index
- Representing discrete intervals using IntervalIndex
- Categorical values as an index - CategoricalIndex
- Indexing by date and time using DatetimeIndex
- Indexing periods of time using PeriodIndex
- Working with Indexes
- Hierarchical indexing
- Summary
- Categorical Data
- Numerical and Statistical Methods
- Configuring pandas
- Performing numerical methods on pandas objects
- Performing statistical processes on pandas objects
- Retrieving summary descriptive statistics
- Measuring central tendency: mean, median, and mode
- Calculating variance and standard deviation
- Determining covariance and correlation
- Performing discretization and quantiling of data
- Calculating the rank of values
- Calculating the percent change at each sample of a series
- Performing moving-window operations
- Executing random sampling of data
- Summary
- Accessing Data
- Configuring pandas
- Working with CSV and text/tabular format data
- Examining the sample CSV data set
- Reading a CSV file into a DataFrame
- Specifying the index column when reading a CSV file
- Data type inference and specification
- Specifying column names
- Specifying specific columns to load
- Saving DataFrame to a CSV file
- Working with general field-delimited data
- Handling variants of formats in field-delimited data
- Reading and writing data in Excel format
- Reading and writing JSON files
- Reading HTML data from the web
- Reading and writing HDF5 format files
- Accessing CSV data on the web
- Reading and writing from/to SQL databases
- Reading data from remote data services
- Summary
- Tidying Up Your Data
- Combining, Relating, and Reshaping Data
- Data Aggregation
- Time-Series Modelling
- Setting up the IPython notebook
- Representation of dates, time, and intervals
- Introducing time-series data
- Calculating new dates using offsets
- Representing durations of time using Period
- Handling holidays using calendars
- Normalizing timestamps using time zones
- Manipulating time-series data
- Time-series moving-window operations
- Summary
- Visualization
- Configuring pandas
- Plotting basics with pandas
- Creating time-series charts
- Common plots used in statistical analyses
- Showing relative differences with bar plots
- Picturing distributions of data with histograms
- Depicting distributions of categorical data with box and whisker charts
- Demonstrating cumulative totals with area plots
- Relationships between two variables with scatter plots
- Estimates of distribution with the kernel density plot
- Correlations between multiple variables with the scatter plot matrix
- Strengths of relationships in multiple variables with heatmaps
- Manually rendering multiple plots in a single chart
- Summary
- Historical Stock Price Analysis
- Setting up the IPython notebook
- Obtaining and organizing stock data from Google
- Plotting time-series prices
- Plotting volume-series data
- Calculating the simple daily percentage change in closing price
- Calculating simple daily cumulative returns of a stock
- Resampling data from daily to monthly returns
- Analyzing distribution of returns
- Performing a moving-average calculation
- Comparison of average daily returns across stocks
- Correlation of stocks based on the daily percentage change of the closing price
- Calculating the volatility of stocks
- Determining risk relative to expected returns
- Summary
Removing categories
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