Pandas Doc
1

Table of Contents

  • API Reference
    • Input/Output
      • Pickling
        • pandas.read_pickle
      • Flat File
        • pandas.read_table
        • pandas.read_csv
        • pandas.read_fwf
      • Clipboard
        • pandas.read_clipboard
      • Excel
        • pandas.read_excel
        • pandas.ExcelFile.parse
      • JSON
        • pandas.read_json
        • pandas.io.json.json_normalize
      • HTML
        • pandas.read_html
      • HDFStore: PyTables (HDF5)
        • pandas.read_hdf
        • pandas.HDFStore.put
        • pandas.HDFStore.append
        • pandas.HDFStore.get
        • pandas.HDFStore.select
      • SAS
        • pandas.read_sas
      • SQL
        • pandas.read_sql_table
        • pandas.read_sql_query
        • pandas.read_sql
      • Google BigQuery
        • pandas.io.gbq.read_gbq
        • pandas.io.gbq.to_gbq
      • STATA
        • pandas.read_stata
        • pandas.io.stata.StataReader.data
        • pandas.io.stata.StataReader.data_label
        • pandas.io.stata.StataReader.value_labels
        • pandas.io.stata.StataReader.variable_labels
        • pandas.io.stata.StataWriter.write_file
    • General functions
      • Data manipulations
        • pandas.melt
        • pandas.pivot
        • pandas.pivot_table
        • pandas.crosstab
        • pandas.cut
        • pandas.qcut
        • pandas.merge
        • pandas.merge_ordered
        • pandas.merge_asof
        • pandas.concat
        • pandas.get_dummies
        • pandas.factorize
      • Top-level missing data
        • pandas.isnull
        • pandas.notnull
      • Top-level conversions
        • pandas.to_numeric
      • Top-level dealing with datetimelike
        • pandas.to_datetime
        • pandas.to_timedelta
        • pandas.date_range
        • pandas.bdate_range
        • pandas.period_range
        • pandas.timedelta_range
        • pandas.infer_freq
      • Top-level evaluation
        • pandas.eval
      • Testing
        • pandas.test
    • Series
      • Constructor
        • pandas.Series
      • Attributes
        • pandas.Series.values
        • pandas.Series.dtype
        • pandas.Series.ftype
        • pandas.Series.shape
        • pandas.Series.nbytes
        • pandas.Series.ndim
        • pandas.Series.size
        • pandas.Series.strides
        • pandas.Series.itemsize
        • pandas.Series.base
        • pandas.Series.T
        • pandas.Series.memory_usage
      • Conversion
        • pandas.Series.astype
        • pandas.Series.copy
        • pandas.Series.isnull
        • pandas.Series.notnull
      • Indexing, iteration
        • pandas.Series.get
        • pandas.Series.at
        • pandas.Series.iat
        • pandas.Series.ix
        • pandas.Series.loc
        • pandas.Series.iloc
        • pandas.Series.__iter__
        • pandas.Series.iteritems
      • Binary operator functions
        • pandas.Series.add
        • pandas.Series.sub
        • pandas.Series.mul
        • pandas.Series.div
        • pandas.Series.truediv
        • pandas.Series.floordiv
        • pandas.Series.mod
        • pandas.Series.pow
        • pandas.Series.radd
        • pandas.Series.rsub
        • pandas.Series.rmul
        • pandas.Series.rdiv
        • pandas.Series.rtruediv
        • pandas.Series.rfloordiv
        • pandas.Series.rmod
        • pandas.Series.rpow
        • pandas.Series.combine
        • pandas.Series.combine_first
        • pandas.Series.round
        • pandas.Series.lt
        • pandas.Series.gt
        • pandas.Series.le
        • pandas.Series.ge
        • pandas.Series.ne
        • pandas.Series.eq
      • Function application, GroupBy & Window
        • pandas.Series.apply
        • pandas.Series.map
        • pandas.Series.groupby
        • pandas.Series.rolling
        • pandas.Series.expanding
        • pandas.Series.ewm
      • Computations / Descriptive Stats
        • pandas.Series.abs
        • pandas.Series.all
        • pandas.Series.any
        • pandas.Series.autocorr
        • pandas.Series.between
        • pandas.Series.clip
        • pandas.Series.clip_lower
        • pandas.Series.clip_upper
        • pandas.Series.corr
        • pandas.Series.count
        • pandas.Series.cov
        • pandas.Series.cummax
        • pandas.Series.cummin
        • pandas.Series.cumprod
        • pandas.Series.cumsum
        • pandas.Series.describe
        • pandas.Series.diff
        • pandas.Series.factorize
        • pandas.Series.kurt
        • pandas.Series.mad
        • pandas.Series.max
        • pandas.Series.mean
        • pandas.Series.median
        • pandas.Series.min
        • pandas.Series.mode
        • pandas.Series.nlargest
        • pandas.Series.nsmallest
        • pandas.Series.pct_change
        • pandas.Series.prod
        • pandas.Series.quantile
        • pandas.Series.rank
        • pandas.Series.sem
        • pandas.Series.skew
        • pandas.Series.std
        • pandas.Series.sum
        • pandas.Series.var
        • pandas.Series.unique
        • pandas.Series.nunique
        • pandas.Series.is_unique
        • pandas.Series.is_monotonic
        • pandas.Series.is_monotonic_increasing
        • pandas.Series.is_monotonic_decreasing
        • pandas.Series.value_counts
      • Reindexing / Selection / Label manipulation
        • pandas.Series.align
        • pandas.Series.drop
        • pandas.Series.drop_duplicates
        • pandas.Series.duplicated
        • pandas.Series.equals
        • pandas.Series.first
        • pandas.Series.head
        • pandas.Series.idxmax
        • pandas.Series.idxmin
        • pandas.Series.isin
        • pandas.Series.last
        • pandas.Series.reindex
        • pandas.Series.reindex_like
        • pandas.Series.rename
        • pandas.Series.rename_axis
        • pandas.Series.reset_index
        • pandas.Series.sample
        • pandas.Series.select
        • pandas.Series.take
        • pandas.Series.tail
        • pandas.Series.truncate
        • pandas.Series.where
        • pandas.Series.mask
      • Missing data handling
        • pandas.Series.dropna
        • pandas.Series.fillna
        • pandas.Series.interpolate
      • Reshaping, sorting
        • pandas.Series.argsort
        • pandas.Series.reorder_levels
        • pandas.Series.sort_values
        • pandas.Series.sort_index
        • pandas.Series.sortlevel
        • pandas.Series.swaplevel
        • pandas.Series.unstack
        • pandas.Series.searchsorted
      • Combining / joining / merging
        • pandas.Series.append
        • pandas.Series.replace
        • pandas.Series.update
      • Time series-related
        • pandas.Series.asfreq
        • pandas.Series.asof
        • pandas.Series.shift
        • pandas.Series.first_valid_index
        • pandas.Series.last_valid_index
        • pandas.Series.resample
        • pandas.Series.tz_convert
        • pandas.Series.tz_localize
      • Datetimelike Properties
        • pandas.Series.dt.date
        • pandas.Series.dt.time
        • pandas.Series.dt.year
        • pandas.Series.dt.month
        • pandas.Series.dt.day
        • pandas.Series.dt.hour
        • pandas.Series.dt.minute
        • pandas.Series.dt.second
        • pandas.Series.dt.microsecond
        • pandas.Series.dt.nanosecond
        • pandas.Series.dt.week
        • pandas.Series.dt.weekofyear
        • pandas.Series.dt.dayofweek
        • pandas.Series.dt.weekday
        • pandas.Series.dt.weekday_name
        • pandas.Series.dt.dayofyear
        • pandas.Series.dt.quarter
        • pandas.Series.dt.is_month_start
        • pandas.Series.dt.is_month_end
        • pandas.Series.dt.is_quarter_start
        • pandas.Series.dt.is_quarter_end
        • pandas.Series.dt.is_year_start
        • pandas.Series.dt.is_year_end
        • pandas.Series.dt.is_leap_year
        • pandas.Series.dt.daysinmonth
        • pandas.Series.dt.days_in_month
        • pandas.Series.dt.tz
        • pandas.Series.dt.freq
        • pandas.Series.dt.to_period
        • pandas.Series.dt.to_pydatetime
        • pandas.Series.dt.tz_localize
        • pandas.Series.dt.tz_convert
        • pandas.Series.dt.normalize
        • pandas.Series.dt.strftime
        • pandas.Series.dt.round
        • pandas.Series.dt.floor
        • pandas.Series.dt.ceil
        • pandas.Series.dt.days
        • pandas.Series.dt.seconds
        • pandas.Series.dt.microseconds
        • pandas.Series.dt.nanoseconds
        • pandas.Series.dt.components
        • pandas.Series.dt.to_pytimedelta
        • pandas.Series.dt.total_seconds
      • String handling
        • pandas.Series.str.capitalize
        • pandas.Series.str.cat
        • pandas.Series.str.center
        • pandas.Series.str.contains
        • pandas.Series.str.count
        • pandas.Series.str.decode
        • pandas.Series.str.encode
        • pandas.Series.str.endswith
        • pandas.Series.str.extract
        • pandas.Series.str.extractall
        • pandas.Series.str.find
        • pandas.Series.str.findall
        • pandas.Series.str.get
        • pandas.Series.str.index
        • pandas.Series.str.join
        • pandas.Series.str.len
        • pandas.Series.str.ljust
        • pandas.Series.str.lower
        • pandas.Series.str.lstrip
        • pandas.Series.str.match
        • pandas.Series.str.normalize
        • pandas.Series.str.pad
        • pandas.Series.str.partition
        • pandas.Series.str.repeat
        • pandas.Series.str.replace
        • pandas.Series.str.rfind
        • pandas.Series.str.rindex
        • pandas.Series.str.rjust
        • pandas.Series.str.rpartition
        • pandas.Series.str.rstrip
        • pandas.Series.str.slice
        • pandas.Series.str.slice_replace
        • pandas.Series.str.split
        • pandas.Series.str.rsplit
        • pandas.Series.str.startswith
        • pandas.Series.str.strip
        • pandas.Series.str.swapcase
        • pandas.Series.str.title
        • pandas.Series.str.translate
        • pandas.Series.str.upper
        • pandas.Series.str.wrap
        • pandas.Series.str.zfill
        • pandas.Series.str.isalnum
        • pandas.Series.str.isalpha
        • pandas.Series.str.isdigit
        • pandas.Series.str.isspace
        • pandas.Series.str.islower
        • pandas.Series.str.isupper
        • pandas.Series.str.istitle
        • pandas.Series.str.isnumeric
        • pandas.Series.str.isdecimal
        • pandas.Series.str.get_dummies
      • Categorical
        • pandas.Series.cat.categories
        • pandas.Series.cat.ordered
        • pandas.Series.cat.codes
        • pandas.Series.cat.rename_categories
        • pandas.Series.cat.reorder_categories
        • pandas.Series.cat.add_categories
        • pandas.Series.cat.remove_categories
        • pandas.Series.cat.remove_unused_categories
        • pandas.Series.cat.set_categories
        • pandas.Series.cat.as_ordered
        • pandas.Series.cat.as_unordered
        • pandas.Categorical
        • pandas.Categorical.from_codes
        • pandas.Categorical.__array__
      • Plotting
        • pandas.Series.plot
        • pandas.Series.plot.area
        • pandas.Series.plot.bar
        • pandas.Series.plot.barh
        • pandas.Series.plot.box
        • pandas.Series.plot.density
        • pandas.Series.plot.hist
        • pandas.Series.plot.kde
        • pandas.Series.plot.line
        • pandas.Series.plot.pie
        • pandas.Series.hist
      • Serialization / IO / Conversion
        • pandas.Series.from_csv
        • pandas.Series.to_pickle
        • pandas.Series.to_csv
        • pandas.Series.to_dict
        • pandas.Series.to_frame
        • pandas.Series.to_xarray
        • pandas.Series.to_hdf
        • pandas.Series.to_sql
        • pandas.Series.to_msgpack
        • pandas.Series.to_json
        • pandas.Series.to_sparse
        • pandas.Series.to_dense
        • pandas.Series.to_string
        • pandas.Series.to_clipboard
      • Sparse methods
        • pandas.SparseSeries.to_coo
        • pandas.SparseSeries.from_coo
    • DataFrame
      • Constructor
        • pandas.DataFrame
      • Attributes and underlying data
        • pandas.DataFrame.as_matrix
        • pandas.DataFrame.dtypes
        • pandas.DataFrame.ftypes
        • pandas.DataFrame.get_dtype_counts
        • pandas.DataFrame.get_ftype_counts
        • pandas.DataFrame.select_dtypes
        • pandas.DataFrame.values
        • pandas.DataFrame.axes
        • pandas.DataFrame.ndim
        • pandas.DataFrame.size
        • pandas.DataFrame.shape
        • pandas.DataFrame.memory_usage
      • Conversion
        • pandas.DataFrame.astype
        • pandas.DataFrame.convert_objects
        • pandas.DataFrame.copy
        • pandas.DataFrame.isnull
        • pandas.DataFrame.notnull
      • Indexing, iteration
        • pandas.DataFrame.head
        • pandas.DataFrame.at
        • pandas.DataFrame.iat
        • pandas.DataFrame.ix
        • pandas.DataFrame.loc
        • pandas.DataFrame.iloc
        • pandas.DataFrame.insert
        • pandas.DataFrame.__iter__
        • pandas.DataFrame.iteritems
        • pandas.DataFrame.iterrows
        • pandas.DataFrame.itertuples
        • pandas.DataFrame.lookup
        • pandas.DataFrame.pop
        • pandas.DataFrame.tail
        • pandas.DataFrame.xs
        • pandas.DataFrame.isin
        • pandas.DataFrame.where
        • pandas.DataFrame.mask
        • pandas.DataFrame.query
      • Binary operator functions
        • pandas.DataFrame.add
        • pandas.DataFrame.sub
        • pandas.DataFrame.mul
        • pandas.DataFrame.div
        • pandas.DataFrame.truediv
        • pandas.DataFrame.floordiv
        • pandas.DataFrame.mod
        • pandas.DataFrame.pow
        • pandas.DataFrame.radd
        • pandas.DataFrame.rsub
        • pandas.DataFrame.rmul
        • pandas.DataFrame.rdiv
        • pandas.DataFrame.rtruediv
        • pandas.DataFrame.rfloordiv
        • pandas.DataFrame.rmod
        • pandas.DataFrame.rpow
        • pandas.DataFrame.lt
        • pandas.DataFrame.gt
        • pandas.DataFrame.le
        • pandas.DataFrame.ge
        • pandas.DataFrame.ne
        • pandas.DataFrame.eq
        • pandas.DataFrame.combine
        • pandas.DataFrame.combine_first
      • Function application, GroupBy & Window
        • pandas.DataFrame.apply
        • pandas.DataFrame.applymap
        • pandas.DataFrame.groupby
        • pandas.DataFrame.rolling
        • pandas.DataFrame.expanding
        • pandas.DataFrame.ewm
      • Computations / Descriptive Stats
        • pandas.DataFrame.abs
        • pandas.DataFrame.all
        • pandas.DataFrame.any
        • pandas.DataFrame.clip
        • pandas.DataFrame.clip_lower
        • pandas.DataFrame.clip_upper
        • pandas.DataFrame.corr
        • pandas.DataFrame.corrwith
        • pandas.DataFrame.count
        • pandas.DataFrame.cov
        • pandas.DataFrame.cummax
        • pandas.DataFrame.cummin
        • pandas.DataFrame.cumprod
        • pandas.DataFrame.cumsum
        • pandas.DataFrame.describe
        • pandas.DataFrame.diff
        • pandas.DataFrame.eval
        • pandas.DataFrame.kurt
        • pandas.DataFrame.mad
        • pandas.DataFrame.max
        • pandas.DataFrame.mean
        • pandas.DataFrame.median
        • pandas.DataFrame.min
        • pandas.DataFrame.mode
        • pandas.DataFrame.pct_change
        • pandas.DataFrame.prod
        • pandas.DataFrame.quantile
        • pandas.DataFrame.rank
        • pandas.DataFrame.round
        • pandas.DataFrame.sem
        • pandas.DataFrame.skew
        • pandas.DataFrame.sum
        • pandas.DataFrame.std
        • pandas.DataFrame.var
      • Reindexing / Selection / Label manipulation
        • pandas.DataFrame.add_prefix
        • pandas.DataFrame.add_suffix
        • pandas.DataFrame.align
        • pandas.DataFrame.drop
        • pandas.DataFrame.drop_duplicates
        • pandas.DataFrame.duplicated
        • pandas.DataFrame.equals
        • pandas.DataFrame.filter
        • pandas.DataFrame.first
        • pandas.DataFrame.head
        • pandas.DataFrame.idxmax
        • pandas.DataFrame.idxmin
        • pandas.DataFrame.last
        • pandas.DataFrame.reindex
        • pandas.DataFrame.reindex_axis
        • pandas.DataFrame.reindex_like
        • pandas.DataFrame.rename
        • pandas.DataFrame.rename_axis
        • pandas.DataFrame.reset_index
        • pandas.DataFrame.sample
        • pandas.DataFrame.select
        • pandas.DataFrame.set_index
        • pandas.DataFrame.tail
        • pandas.DataFrame.take
        • pandas.DataFrame.truncate
      • Missing data handling
        • pandas.DataFrame.dropna
        • pandas.DataFrame.fillna
        • pandas.DataFrame.replace
      • Reshaping, sorting, transposing
        • pandas.DataFrame.pivot
        • pandas.DataFrame.reorder_levels
        • pandas.DataFrame.sort_values
        • pandas.DataFrame.sort_index
        • pandas.DataFrame.sortlevel
        • pandas.DataFrame.nlargest
        • pandas.DataFrame.nsmallest
        • pandas.DataFrame.swaplevel
        • pandas.DataFrame.stack
        • pandas.DataFrame.unstack
        • pandas.DataFrame.T
        • pandas.DataFrame.to_panel
        • pandas.DataFrame.to_xarray
        • pandas.DataFrame.transpose
      • Combining / joining / merging
        • pandas.DataFrame.append
        • pandas.DataFrame.assign
        • pandas.DataFrame.join
        • pandas.DataFrame.merge
        • pandas.DataFrame.update
      • Time series-related
        • pandas.DataFrame.asfreq
        • pandas.DataFrame.asof
        • pandas.DataFrame.shift
        • pandas.DataFrame.first_valid_index
        • pandas.DataFrame.last_valid_index
        • pandas.DataFrame.resample
        • pandas.DataFrame.to_period
        • pandas.DataFrame.to_timestamp
        • pandas.DataFrame.tz_convert
        • pandas.DataFrame.tz_localize
      • Plotting
        • pandas.DataFrame.plot
        • pandas.DataFrame.plot.area
        • pandas.DataFrame.plot.bar
        • pandas.DataFrame.plot.barh
        • pandas.DataFrame.plot.box
        • pandas.DataFrame.plot.density
        • pandas.DataFrame.plot.hexbin
        • pandas.DataFrame.plot.hist
        • pandas.DataFrame.plot.kde
        • pandas.DataFrame.plot.line
        • pandas.DataFrame.plot.pie
        • pandas.DataFrame.plot.scatter
        • pandas.DataFrame.boxplot
        • pandas.DataFrame.hist
      • Serialization / IO / Conversion
        • pandas.DataFrame.from_csv
        • pandas.DataFrame.from_dict
        • pandas.DataFrame.from_items
        • pandas.DataFrame.from_records
        • pandas.DataFrame.info
        • pandas.DataFrame.to_pickle
        • pandas.DataFrame.to_csv
        • pandas.DataFrame.to_hdf
        • pandas.DataFrame.to_sql
        • pandas.DataFrame.to_dict
        • pandas.DataFrame.to_excel
        • pandas.DataFrame.to_json
        • pandas.DataFrame.to_html
        • pandas.DataFrame.to_latex
        • pandas.DataFrame.to_stata
        • pandas.DataFrame.to_msgpack
        • pandas.DataFrame.to_gbq
        • pandas.DataFrame.to_records
        • pandas.DataFrame.to_sparse
        • pandas.DataFrame.to_dense
        • pandas.DataFrame.to_string
        • pandas.DataFrame.to_clipboard
    • Panel
      • Constructor
        • pandas.Panel
      • Attributes and underlying data
        • pandas.Panel.values
        • pandas.Panel.axes
        • pandas.Panel.ndim
        • pandas.Panel.size
        • pandas.Panel.shape
        • pandas.Panel.dtypes
        • pandas.Panel.ftypes
        • pandas.Panel.get_dtype_counts
        • pandas.Panel.get_ftype_counts
      • Conversion
        • pandas.Panel.astype
        • pandas.Panel.copy
        • pandas.Panel.isnull
        • pandas.Panel.notnull
      • Getting and setting
        • pandas.Panel.get_value
        • pandas.Panel.set_value
      • Indexing, iteration, slicing
        • pandas.Panel.at
        • pandas.Panel.iat
        • pandas.Panel.ix
        • pandas.Panel.loc
        • pandas.Panel.iloc
        • pandas.Panel.__iter__
        • pandas.Panel.iteritems
        • pandas.Panel.pop
        • pandas.Panel.xs
        • pandas.Panel.major_xs
        • pandas.Panel.minor_xs
      • Binary operator functions
        • pandas.Panel.add
        • pandas.Panel.sub
        • pandas.Panel.mul
        • pandas.Panel.div
        • pandas.Panel.truediv
        • pandas.Panel.floordiv
        • pandas.Panel.mod
        • pandas.Panel.pow
        • pandas.Panel.radd
        • pandas.Panel.rsub
        • pandas.Panel.rmul
        • pandas.Panel.rdiv
        • pandas.Panel.rtruediv
        • pandas.Panel.rfloordiv
        • pandas.Panel.rmod
        • pandas.Panel.rpow
        • pandas.Panel.lt
        • pandas.Panel.gt
        • pandas.Panel.le
        • pandas.Panel.ge
        • pandas.Panel.ne
        • pandas.Panel.eq
      • Function application, GroupBy
        • pandas.Panel.apply
        • pandas.Panel.groupby
      • Computations / Descriptive Stats
        • pandas.Panel.abs
        • pandas.Panel.clip
        • pandas.Panel.clip_lower
        • pandas.Panel.clip_upper
        • pandas.Panel.count
        • pandas.Panel.cummax
        • pandas.Panel.cummin
        • pandas.Panel.cumprod
        • pandas.Panel.cumsum
        • pandas.Panel.max
        • pandas.Panel.mean
        • pandas.Panel.median
        • pandas.Panel.min
        • pandas.Panel.pct_change
        • pandas.Panel.prod
        • pandas.Panel.sem
        • pandas.Panel.skew
        • pandas.Panel.sum
        • pandas.Panel.std
        • pandas.Panel.var
      • Reindexing / Selection / Label manipulation
        • pandas.Panel.add_prefix
        • pandas.Panel.add_suffix
        • pandas.Panel.drop
        • pandas.Panel.equals
        • pandas.Panel.filter
        • pandas.Panel.first
        • pandas.Panel.last
        • pandas.Panel.reindex
        • pandas.Panel.reindex_axis
        • pandas.Panel.reindex_like
        • pandas.Panel.rename
        • pandas.Panel.sample
        • pandas.Panel.select
        • pandas.Panel.take
        • pandas.Panel.truncate
      • Missing data handling
        • pandas.Panel.dropna
        • pandas.Panel.fillna
      • Reshaping, sorting, transposing
        • pandas.Panel.sort_index
        • pandas.Panel.swaplevel
        • pandas.Panel.transpose
        • pandas.Panel.swapaxes
        • pandas.Panel.conform
      • Combining / joining / merging
        • pandas.Panel.join
        • pandas.Panel.update
      • Time series-related
        • pandas.Panel.asfreq
        • pandas.Panel.shift
        • pandas.Panel.resample
        • pandas.Panel.tz_convert
        • pandas.Panel.tz_localize
      • Serialization / IO / Conversion
        • pandas.Panel.from_dict
        • pandas.Panel.to_pickle
        • pandas.Panel.to_excel
        • pandas.Panel.to_hdf
        • pandas.Panel.to_sparse
        • pandas.Panel.to_frame
        • pandas.Panel.to_xarray
        • pandas.Panel.to_clipboard
    • Panel4D
      • Constructor
        • pandas.Panel4D
      • Serialization / IO / Conversion
        • pandas.Panel4D.to_xarray
      • Attributes and underlying data
        • pandas.Panel4D.values
        • pandas.Panel4D.axes
        • pandas.Panel4D.ndim
        • pandas.Panel4D.size
        • pandas.Panel4D.shape
        • pandas.Panel4D.dtypes
        • pandas.Panel4D.ftypes
        • pandas.Panel4D.get_dtype_counts
        • pandas.Panel4D.get_ftype_counts
      • Conversion
        • pandas.Panel4D.astype
        • pandas.Panel4D.copy
        • pandas.Panel4D.isnull
        • pandas.Panel4D.notnull
    • Index
      • pandas.Index
      • Attributes
        • pandas.Index.values
        • pandas.Index.is_monotonic
        • pandas.Index.is_monotonic_increasing
        • pandas.Index.is_monotonic_decreasing
        • pandas.Index.is_unique
        • pandas.Index.has_duplicates
        • pandas.Index.dtype
        • pandas.Index.inferred_type
        • pandas.Index.is_all_dates
        • pandas.Index.shape
        • pandas.Index.nbytes
        • pandas.Index.ndim
        • pandas.Index.size
        • pandas.Index.strides
        • pandas.Index.itemsize
        • pandas.Index.base
        • pandas.Index.T
        • pandas.Index.memory_usage
      • Modifying and Computations
        • pandas.Index.all
        • pandas.Index.any
        • pandas.Index.argmin
        • pandas.Index.argmax
        • pandas.Index.copy
        • pandas.Index.delete
        • pandas.Index.drop
        • pandas.Index.drop_duplicates
        • pandas.Index.duplicated
        • pandas.Index.equals
        • pandas.Index.factorize
        • pandas.Index.identical
        • pandas.Index.insert
        • pandas.Index.min
        • pandas.Index.max
        • pandas.Index.reindex
        • pandas.Index.repeat
        • pandas.Index.where
        • pandas.Index.take
        • pandas.Index.putmask
        • pandas.Index.set_names
        • pandas.Index.unique
        • pandas.Index.nunique
        • pandas.Index.value_counts
        • pandas.Index.fillna
        • pandas.Index.dropna
      • Conversion
        • pandas.Index.astype
        • pandas.Index.tolist
        • pandas.Index.to_datetime
        • pandas.Index.to_series
      • Sorting
        • pandas.Index.argsort
        • pandas.Index.sort_values
      • Time-specific operations
        • pandas.Index.shift
      • Combining / joining / set operations
        • pandas.Index.append
        • pandas.Index.join
        • pandas.Index.intersection
        • pandas.Index.union
        • pandas.Index.difference
        • pandas.Index.symmetric_difference
      • Selecting
        • pandas.Index.get_indexer
        • pandas.Index.get_indexer_non_unique
        • pandas.Index.get_level_values
        • pandas.Index.get_loc
        • pandas.Index.get_value
        • pandas.Index.isin
        • pandas.Index.slice_indexer
        • pandas.Index.slice_locs
    • CategoricalIndex
      • pandas.CategoricalIndex
      • Categorical Components
        • pandas.CategoricalIndex.codes
        • pandas.CategoricalIndex.categories
        • pandas.CategoricalIndex.ordered
        • pandas.CategoricalIndex.rename_categories
        • pandas.CategoricalIndex.reorder_categories
        • pandas.CategoricalIndex.add_categories
        • pandas.CategoricalIndex.remove_categories
        • pandas.CategoricalIndex.remove_unused_categories
        • pandas.CategoricalIndex.set_categories
        • pandas.CategoricalIndex.as_ordered
        • pandas.CategoricalIndex.as_unordered
    • MultiIndex
      • pandas.MultiIndex
      • MultiIndex Components
        • pandas.MultiIndex.from_arrays
        • pandas.MultiIndex.from_tuples
        • pandas.MultiIndex.from_product
        • pandas.MultiIndex.set_levels
        • pandas.MultiIndex.set_labels
        • pandas.MultiIndex.to_hierarchical
        • pandas.MultiIndex.is_lexsorted
        • pandas.MultiIndex.droplevel
        • pandas.MultiIndex.swaplevel
        • pandas.MultiIndex.reorder_levels
    • DatetimeIndex
      • pandas.DatetimeIndex
      • Time/Date Components
        • pandas.DatetimeIndex.year
        • pandas.DatetimeIndex.month
        • pandas.DatetimeIndex.day
        • pandas.DatetimeIndex.hour
        • pandas.DatetimeIndex.minute
        • pandas.DatetimeIndex.second
        • pandas.DatetimeIndex.microsecond
        • pandas.DatetimeIndex.nanosecond
        • pandas.DatetimeIndex.date
        • pandas.DatetimeIndex.time
        • pandas.DatetimeIndex.dayofyear
        • pandas.DatetimeIndex.weekofyear
        • pandas.DatetimeIndex.week
        • pandas.DatetimeIndex.dayofweek
        • pandas.DatetimeIndex.weekday
        • pandas.DatetimeIndex.weekday_name
        • pandas.DatetimeIndex.quarter
        • pandas.DatetimeIndex.tz
        • pandas.DatetimeIndex.freq
        • pandas.DatetimeIndex.freqstr
        • pandas.DatetimeIndex.is_month_start
        • pandas.DatetimeIndex.is_month_end
        • pandas.DatetimeIndex.is_quarter_start
        • pandas.DatetimeIndex.is_quarter_end
        • pandas.DatetimeIndex.is_year_start
        • pandas.DatetimeIndex.is_year_end
        • pandas.DatetimeIndex.is_leap_year
        • pandas.DatetimeIndex.inferred_freq
      • Selecting
        • pandas.DatetimeIndex.indexer_at_time
        • pandas.DatetimeIndex.indexer_between_time
      • Time-specific operations
        • pandas.DatetimeIndex.normalize
        • pandas.DatetimeIndex.strftime
        • pandas.DatetimeIndex.snap
        • pandas.DatetimeIndex.tz_convert
        • pandas.DatetimeIndex.tz_localize
        • pandas.DatetimeIndex.round
        • pandas.DatetimeIndex.floor
        • pandas.DatetimeIndex.ceil
      • Conversion
        • pandas.DatetimeIndex.to_datetime
        • pandas.DatetimeIndex.to_period
        • pandas.DatetimeIndex.to_perioddelta
        • pandas.DatetimeIndex.to_pydatetime
        • pandas.DatetimeIndex.to_series
    • TimedeltaIndex
      • pandas.TimedeltaIndex
      • Components
        • pandas.TimedeltaIndex.days
        • pandas.TimedeltaIndex.seconds
        • pandas.TimedeltaIndex.microseconds
        • pandas.TimedeltaIndex.nanoseconds
        • pandas.TimedeltaIndex.components
        • pandas.TimedeltaIndex.inferred_freq
      • Conversion
        • pandas.TimedeltaIndex.to_pytimedelta
        • pandas.TimedeltaIndex.to_series
        • pandas.TimedeltaIndex.round
        • pandas.TimedeltaIndex.floor
        • pandas.TimedeltaIndex.ceil
    • Window
      • Standard moving window functions
        • pandas.core.window.Rolling.count
        • pandas.core.window.Rolling.sum
        • pandas.core.window.Rolling.mean
        • pandas.core.window.Rolling.median
        • pandas.core.window.Rolling.var
        • pandas.core.window.Rolling.std
        • pandas.core.window.Rolling.min
        • pandas.core.window.Rolling.max
        • pandas.core.window.Rolling.corr
        • pandas.core.window.Rolling.cov
        • pandas.core.window.Rolling.skew
        • pandas.core.window.Rolling.kurt
        • pandas.core.window.Rolling.apply
        • pandas.core.window.Rolling.quantile
        • pandas.core.window.Window.mean
        • pandas.core.window.Window.sum
      • Standard expanding window functions
        • pandas.core.window.Expanding.count
        • pandas.core.window.Expanding.sum
        • pandas.core.window.Expanding.mean
        • pandas.core.window.Expanding.median
        • pandas.core.window.Expanding.var
        • pandas.core.window.Expanding.std
        • pandas.core.window.Expanding.min
        • pandas.core.window.Expanding.max
        • pandas.core.window.Expanding.corr
        • pandas.core.window.Expanding.cov
        • pandas.core.window.Expanding.skew
        • pandas.core.window.Expanding.kurt
        • pandas.core.window.Expanding.apply
        • pandas.core.window.Expanding.quantile
      • Exponentially-weighted moving window functions
        • pandas.core.window.EWM.mean
        • pandas.core.window.EWM.std
        • pandas.core.window.EWM.var
        • pandas.core.window.EWM.corr
        • pandas.core.window.EWM.cov
    • GroupBy
      • Indexing, iteration
        • pandas.core.groupby.GroupBy.__iter__
        • pandas.core.groupby.GroupBy.groups
        • pandas.core.groupby.GroupBy.indices
        • pandas.core.groupby.GroupBy.get_group
        • pandas.Grouper
      • Function application
        • pandas.core.groupby.GroupBy.apply
        • pandas.core.groupby.GroupBy.aggregate
        • pandas.core.groupby.GroupBy.transform
      • Computations / Descriptive Stats
        • pandas.core.groupby.GroupBy.count
        • pandas.core.groupby.GroupBy.cumcount
        • pandas.core.groupby.GroupBy.first
        • pandas.core.groupby.GroupBy.head
        • pandas.core.groupby.GroupBy.last
        • pandas.core.groupby.GroupBy.max
        • pandas.core.groupby.GroupBy.mean
        • pandas.core.groupby.GroupBy.median
        • pandas.core.groupby.GroupBy.min
        • pandas.core.groupby.GroupBy.nth
        • pandas.core.groupby.GroupBy.ohlc
        • pandas.core.groupby.GroupBy.prod
        • pandas.core.groupby.GroupBy.size
        • pandas.core.groupby.GroupBy.sem
        • pandas.core.groupby.GroupBy.std
        • pandas.core.groupby.GroupBy.sum
        • pandas.core.groupby.GroupBy.var
        • pandas.core.groupby.GroupBy.tail
        • pandas.core.groupby.DataFrameGroupBy.agg
        • pandas.core.groupby.DataFrameGroupBy.all
        • pandas.core.groupby.DataFrameGroupBy.any
        • pandas.core.groupby.DataFrameGroupBy.bfill
        • pandas.core.groupby.DataFrameGroupBy.corr
        • pandas.core.groupby.DataFrameGroupBy.count
        • pandas.core.groupby.DataFrameGroupBy.cov
        • pandas.core.groupby.DataFrameGroupBy.cummax
        • pandas.core.groupby.DataFrameGroupBy.cummin
        • pandas.core.groupby.DataFrameGroupBy.cumprod
        • pandas.core.groupby.DataFrameGroupBy.cumsum
        • pandas.core.groupby.DataFrameGroupBy.describe
        • pandas.core.groupby.DataFrameGroupBy.diff
        • pandas.core.groupby.DataFrameGroupBy.ffill
        • pandas.core.groupby.DataFrameGroupBy.fillna
        • pandas.core.groupby.DataFrameGroupBy.hist
        • pandas.core.groupby.DataFrameGroupBy.idxmax
        • pandas.core.groupby.DataFrameGroupBy.idxmin
        • pandas.core.groupby.DataFrameGroupBy.mad
        • pandas.core.groupby.DataFrameGroupBy.pct_change
        • pandas.core.groupby.DataFrameGroupBy.plot
        • pandas.core.groupby.DataFrameGroupBy.quantile
        • pandas.core.groupby.DataFrameGroupBy.rank
        • pandas.core.groupby.DataFrameGroupBy.resample
        • pandas.core.groupby.DataFrameGroupBy.shift
        • pandas.core.groupby.DataFrameGroupBy.size
        • pandas.core.groupby.DataFrameGroupBy.skew
        • pandas.core.groupby.DataFrameGroupBy.take
        • pandas.core.groupby.DataFrameGroupBy.tshift
        • pandas.core.groupby.SeriesGroupBy.nlargest
        • pandas.core.groupby.SeriesGroupBy.nsmallest
        • pandas.core.groupby.SeriesGroupBy.nunique
        • pandas.core.groupby.SeriesGroupBy.unique
        • pandas.core.groupby.SeriesGroupBy.value_counts
        • pandas.core.groupby.DataFrameGroupBy.corrwith
        • pandas.core.groupby.DataFrameGroupBy.boxplot
    • Resampling
      • Indexing, iteration
        • pandas.tseries.resample.Resampler.__iter__
        • pandas.tseries.resample.Resampler.groups
        • pandas.tseries.resample.Resampler.indices
        • pandas.tseries.resample.Resampler.get_group
      • Function application
        • pandas.tseries.resample.Resampler.apply
        • pandas.tseries.resample.Resampler.aggregate
        • pandas.tseries.resample.Resampler.transform
      • Upsampling
        • pandas.tseries.resample.Resampler.ffill
        • pandas.tseries.resample.Resampler.backfill
        • pandas.tseries.resample.Resampler.bfill
        • pandas.tseries.resample.Resampler.pad
        • pandas.tseries.resample.Resampler.fillna
        • pandas.tseries.resample.Resampler.asfreq
        • pandas.tseries.resample.Resampler.interpolate
      • Computations / Descriptive Stats
        • pandas.tseries.resample.Resampler.count
        • pandas.tseries.resample.Resampler.nunique
        • pandas.tseries.resample.Resampler.first
        • pandas.tseries.resample.Resampler.last
        • pandas.tseries.resample.Resampler.max
        • pandas.tseries.resample.Resampler.mean
        • pandas.tseries.resample.Resampler.median
        • pandas.tseries.resample.Resampler.min
        • pandas.tseries.resample.Resampler.ohlc
        • pandas.tseries.resample.Resampler.prod
        • pandas.tseries.resample.Resampler.size
        • pandas.tseries.resample.Resampler.sem
        • pandas.tseries.resample.Resampler.std
        • pandas.tseries.resample.Resampler.sum
        • pandas.tseries.resample.Resampler.var
    • Style
      • Constructor
        • pandas.formats.style.Styler
      • Style Application
        • pandas.formats.style.Styler.apply
        • pandas.formats.style.Styler.applymap
        • pandas.formats.style.Styler.format
        • pandas.formats.style.Styler.set_precision
        • pandas.formats.style.Styler.set_table_styles
        • pandas.formats.style.Styler.set_caption
        • pandas.formats.style.Styler.set_properties
        • pandas.formats.style.Styler.set_uuid
        • pandas.formats.style.Styler.clear
      • Builtin Styles
        • pandas.formats.style.Styler.highlight_max
        • pandas.formats.style.Styler.highlight_min
        • pandas.formats.style.Styler.highlight_null
        • pandas.formats.style.Styler.background_gradient
        • pandas.formats.style.Styler.bar
      • Style Export and Import
        • pandas.formats.style.Styler.render
        • pandas.formats.style.Styler.export
        • pandas.formats.style.Styler.use
    • General utility functions
      • Working with options
        • pandas.describe_option
        • pandas.reset_option
        • pandas.get_option
        • pandas.set_option
        • pandas.option_context
  • 10 Minutes to pandas
    • 1 Object Creation
    • 2 Viewing Data
    • 3 Selection
      • 3.1 Getting
      • 3.2 Selection by Label
      • 3.3 Selection by Position
      • 3.4 Boolean Indexing
      • 3.5 Setting
    • 4 Missing Data
    • 5 Operations
      • 5.1 Stats
      • 5.2 Apply
      • 5.3 Histogramming
      • 5.4 String Methods
    • 6 Merge
      • 6.1 Concat
      • 6.2 Join
      • 6.3 Append
    • 7 Grouping
    • 8 Reshaping
      • 8.1 Stack
      • 8.2 Pivot Tables
    • 9 Time Series
    • 10 Categoricals
    • 11 Plotting
    • 12 Getting Data In/Out
      • 12.1 CSV
      • 12.2 HDF5
      • 12.3 Excel
    • 13 Gotchas
  • Intro to Data Structures
    • 1 Series
      • 1.1 Series is ndarray-like
      • 1.2 Series is dict-like
      • 1.3 Vectorized operations and label alignment with Series
      • 1.4 Name attribute
    • 2 DataFrame
      • 2.1 From dict of Series or dicts
      • 2.2 From dict of ndarrays / lists
      • 2.3 From structured or record array
      • 2.4 From a list of dicts
      • 2.5 From a dict of tuples
      • 2.6 From a Series
      • 2.7 Alternate Constructors
      • 2.8 Column selection, addition, deletion
      • 2.9 Assigning New Columns in Method Chains
      • 2.10 Indexing / Selection
      • 2.11 Data alignment and arithmetic
      • 2.12 Transposing
      • 2.13 DataFrame interoperability with NumPy functions
      • 2.14 Console display
      • 2.15 DataFrame column attribute access and IPython completion
    • 3 Panel
      • 3.1 From 3D ndarray with optional axis labels
      • 3.2 From dict of DataFrame objects
      • 3.3 From DataFrame using to_panel method
      • 3.4 Item selection / addition / deletion
      • 3.5 Transposing
      • 3.6 Indexing / Selection
      • 3.7 Squeezing
      • 3.8 Conversion to DataFrame
    • 4 Panel4D (Experimental)
      • 4.1 From 4D ndarray with optional axis labels
      • 4.2 From dict of Panel objects
      • 4.3 Slicing
      • 4.4 Transposing
    • 5 PanelND (Experimental)
  • Essential Basic Functionality
    • 1 Head and Tail
    • 2 Attributes and the raw ndarray(s)
    • 3 Accelerated operations
    • 4 Flexible binary operations
      • 4.1 Matching / broadcasting behavior
      • 4.2 Missing data / operations with fill values
      • 4.3 Flexible Comparisons
      • 4.4 Boolean Reductions
      • 4.5 Comparing if objects are equivalent
      • 4.6 Comparing array-like objects
      • 4.7 Combining overlapping data sets
      • 4.8 General DataFrame Combine
    • 5 Descriptive statistics
      • 5.1 Summarizing data: describe
      • 5.2 Index of Min/Max Values
      • 5.3 Value counts (histogramming) / Mode
      • 5.4 Discretization and quantiling
    • 6 Function application
      • 6.1 Tablewise Function Application
      • 6.2 Row or Column-wise Function Application
      • 6.3 Applying elementwise Python functions
      • 6.4 Applying with a Panel
    • 7 Reindexing and altering labels
      • 7.1 Reindexing to align with another object
      • 7.2 Aligning objects with each other with align
      • 7.3 Filling while reindexing
      • 7.4 Limits on filling while reindexing
      • 7.5 Dropping labels from an axis
      • 7.6 Renaming / mapping labels
    • 8 Iteration
      • 8.1 iteritems
      • 8.2 iterrows
      • 8.3 itertuples
    • 9 .dt accessor
    • 10 Vectorized string methods
    • 11 Sorting
      • 11.1 By Index
      • 11.2 By Values
      • 11.3 searchsorted
      • 11.4 smallest / largest values
      • 11.5 Sorting by a multi-index column
    • 12 Copying
    • 13 dtypes
      • 13.1 defaults
      • 13.2 upcasting
      • 13.3 astype
      • 13.4 object conversion
      • 13.5 gotchas
    • 14 Selecting columns based on dtype
  • Part1 (freqeuntly used)
    • 1 Options and Settings
      • 1.1 Overview
      • 1.2 Getting and Setting Options
      • 1.3 Setting Startup Options in python/ipython Environment
      • 1.4 Frequently Used Options
      • 1.5 Available Options
      • 1.6 Number Formatting
      • 1.7 Unicode Formatting
    • 2 Indexing and Selecting Data
      • 2.1 Different Choices for Indexing
      • 2.2 Basics
      • 2.3 Attribute Access
      • 2.4 Slicing ranges
      • 2.5 Selection By Label
      • 2.6 Selection By Position
      • 2.7 Selection By Callable
      • 2.8 Selecting Random Samples
      • 2.9 Setting With Enlargement
      • 2.10 Fast scalar value getting and setting
      • 2.11 Boolean indexing
      • 2.12 Indexing with isin
      • 2.13 The where() Method and Masking
      • 2.14 The query() Method (Experimental)
        • 2.14.1 MultiIndex query() Syntax
        • 2.14.2 query() Use Cases
        • 2.14.3 query() Python versus pandas Syntax Comparison
        • 2.14.4 The in and not in operators
        • 2.14.5 Special use of the == operator with list objects
        • 2.14.6 Boolean Operators
        • 2.14.7 Performance of query()
      • 2.15 Duplicate Data
      • 2.16 Dictionary-like get() method
      • 2.17 The select() Method
      • 2.18 The lookup() Method
      • 2.19 Index objects
        • 2.19.1 Setting metadata
        • 2.19.2 Set operations on Index objects
        • 2.19.3 Missing values
      • 2.20 Set / Reset Index
        • 2.20.1 Set an index
        • 2.20.2 Reset the index
        • 2.20.3 Adding an ad hoc index
      • 2.21 Returning a view versus a copy
        • 2.21.1 Why does assignment fail when using chained indexing?
        • 2.21.2 Evaluation order matters
    • 3 MultiIndex / Advanced Indexing
      • 3.1 Hierarchical indexing (MultiIndex)
        • 3.1.1 Creating a MultiIndex (hierarchical index) object
        • 3.1.2 Reconstructing the level labels
        • 3.1.3 Basic indexing on axis with MultiIndex
        • 3.1.4 Data alignment and using reindex
      • 3.2 Advanced indexing with hierarchical index
        • 3.2.1 Using slicers
        • 3.2.2 Cross-section
        • 3.2.3 Advanced reindexing and alignment
        • 3.2.4 Swapping levels with swaplevel()
        • 3.2.5 Reordering levels with reorder_levels()
      • 3.3 Sorting a MultiIndex
      • 3.4 Take Methods
      • 3.5 Index Types
        • 3.5.1 CategoricalIndex
        • 3.5.2 Int64Index and RangeIndex
        • 3.5.3 Float64Index
    • 4 Working with missing data
      • 4.1 Missing data basics
        • 4.1.1 When / why does data become missing?
        • 4.1.2 Values considered “missing”
      • 4.2 Datetimes
      • 4.3 Inserting missing data
      • 4.4 Calculations with missing data
        • 4.4.1 NA values in GroupBy
      • 4.5 Cleaning / filling missing data
        • 4.5.1 Filling missing values: fillna
        • 4.5.2 Filling with a PandasObject
        • 4.5.3 Dropping axis labels with missing data: dropna
        • 4.5.4 Interpolation
        • 4.5.5 Replacing Generic Values
        • 4.5.6 String/Regular Expression Replacement
        • 4.5.7 Numeric Replacement
      • 4.6 Missing data casting rules and indexing
    • 5 Group By: split-apply-combine
      • 5.1 Introduction
      • 5.2 Splitting an object into groups
        • 5.2.1 GroupBy sorting
        • 5.2.2 GroupBy object attributes
        • 5.2.3 GroupBy with MultiIndex
        • 5.2.4 DataFrame column selection in GroupBy
      • 5.3 Iterating through groups
      • 5.4 Selecting a group
      • 5.5 Aggregation
        • 5.5.1 Applying multiple functions at once
        • 5.5.2 Applying different functions to DataFrame columns
        • 5.5.3 Cython-optimized aggregation functions
      • 5.6 Transformation
      • 5.7 Filtration
      • 5.8 Dispatching to instance methods
      • 5.9 Flexible apply
      • 5.10 Other useful features
        • 5.10.1 Automatic exclusion of “nuisance” columns
        • 5.10.2 NA and NaT group handling
        • 5.10.3 Grouping with ordered factors
        • 5.10.4 Grouping with a Grouper specification
        • 5.10.5 Taking the first rows of each group
        • 5.10.6 Taking the nth row of each group
        • 5.10.7 Enumerate group items
        • 5.10.8 Plotting
      • 5.11 Examples
        • 5.11.1 Regrouping by factor
        • 5.11.2 Groupby by Indexer to ‘resample’ data
        • 5.11.3 Returning a Series to propagate names
    • 6 Merge, join, and concatenate
      • 6.1 Concatenating objects
        • 6.1.1 Set logic on the other axes
        • 6.1.2 Concatenating using append
        • 6.1.3 Ignoring indexes on the concatenation axis
        • 6.1.4 Concatenating with mixed ndims
        • 6.1.5 More concatenating with group keys
        • 6.1.6 Appending rows to a DataFrame
      • 6.2 Database-style DataFrame joining/merging
        • 6.2.1 Brief primer on merge methods (relational algebra)
        • 6.2.2 The merge indicator
        • 6.2.3 Joining on index
        • 6.2.4 Joining key columns on an index
        • 6.2.5 Joining a single Index to a Multi-index
        • 6.2.6 Joining with two multi-indexes
        • 6.2.7 Overlapping value columns
        • 6.2.8 Joining multiple DataFrame or Panel objects
        • 6.2.9 Merging together values within Series or DataFrame columns
      • 6.3 Timeseries friendly merging
        • 6.3.1 Merging Ordered Data
        • 6.3.2 Merging AsOf
    • 7 Reshaping and Pivot Tables
      • 7.1 Reshaping by pivoting DataFrame objects
      • 7.2 Reshaping by stacking and unstacking
        • 7.2.1 Multiple Levels
        • 7.2.2 Missing Data
        • 7.2.3 With a MultiIndex
      • 7.3 Reshaping by Melt
      • 7.4 Combining with stats and GroupBy
      • 7.5 Pivot tables
        • 7.5.1 Adding margins
      • 7.6 Cross tabulations
        • 7.6.1 Normalization
        • 7.6.2 Adding Margins
      • 7.7 Tiling
      • 7.8 Computing indicator / dummy variables
      • 7.9 Factorizing values
    • 8 Visualization
      • 8.1 Basic Plotting: plot
      • 8.2 Other Plots
        • 8.2.1 Bar plots
        • 8.2.2 Histograms
        • 8.2.3 Box Plots
        • 8.2.4 Area Plot
        • 8.2.5 Scatter Plot
        • 8.2.6 Hexagonal Bin Plot
        • 8.2.7 Pie plot
      • 8.3 Plotting with Missing Data
      • 8.4 Plotting Tools
        • 8.4.1 Scatter Matrix Plot
        • 8.4.2 Density Plot
        • 8.4.3 Andrews Curves
        • 8.4.4 Parallel Coordinates
        • 8.4.5 Lag Plot
        • 8.4.6 Autocorrelation Plot
        • 8.4.7 Bootstrap Plot
        • 8.4.8 RadViz
      • 8.5 Plot Formatting
        • 8.5.1 Controlling the Legend
        • 8.5.2 Scales
        • 8.5.3 Plotting on a Secondary Y-axis
        • 8.5.4 Suppressing Tick Resolution Adjustment
        • 8.5.5 Subplots
        • 8.5.6 Using Layout and Targeting Multiple Axes
        • 8.5.7 Plotting With Error Bars
        • 8.5.8 Plotting Tables
        • 8.5.9 Colormaps
      • 8.6 Plotting directly with matplotlib
      • 8.7 Trellis plotting interface
    • 9 Working with Text Data
      • 9.1 Introduction
      • 9.2 Splitting and Replacing Strings
      • 9.3 Indexing with .str
      • 9.4 Extracting Substrings
        • 9.4.1 Extract first match in each subject (extract)
        • 9.4.2 Extract all matches in each subject (extractall)
      • 9.5 Testing for Strings that Match or Contain a Pattern
      • 9.6 Creating Indicator Variables
      • 9.7 Method Summary
    • 10 IO Tools (Text, CSV, HDF5, ...)
      • 10.1 Introduction
      • 10.2 CSV & Text files
        • 10.2.1 Parsing options
        • 10.2.2 Specifying column data types
        • 10.2.3 Naming and Using Columns
        • 10.2.4 Duplicate names parsing
        • 10.2.5 Comments and Empty Lines
        • 10.2.6 Dealing with Unicode Data
        • 10.2.7 Index columns and trailing delimiters
        • 10.2.8 Date Handling
        • 10.2.9 Specifying method for floating-point conversion
        • 10.2.10 Thousand Separators
        • 10.2.11 NA Values
        • 10.2.12 Infinity
        • 10.2.13 Returning Series
        • 10.2.14 Boolean values
        • 10.2.15 Handling “bad” lines
        • 10.2.16 Quoting and Escape Characters
        • 10.2.17 Files with Fixed Width Columns
        • 10.2.18 Indexes
        • 10.2.19 Automatically “sniffing” the delimiter
        • 10.2.20 Iterating through files chunk by chunk
        • 10.2.21 Specifying the parser engine
        • 10.2.22 Writing out Data
      • 10.3 JSON
        • 10.3.1 Writing JSON
        • 10.3.2 Reading JSON
        • 10.3.3 Normalization
        • 10.3.4 Line delimited json
      • 10.4 HTML
        • 10.4.1 Reading HTML Content
        • 10.4.2 Writing to HTML files
      • 10.5 Excel files
        • 10.5.1 Reading Excel Files
        • 10.5.2 Writing Excel Files
        • 10.5.3 Excel writer engines
      • 10.6 Clipboard
      • 10.7 Pickling
      • 10.8 msgpack (experimental)
        • 10.8.1 Read/Write API
      • 10.9 HDF5 (PyTables)
        • 10.9.1 Read/Write API
        • 10.9.2 Fixed Format
        • 10.9.3 Table Format
        • 10.9.4 Hierarchical Keys
        • 10.9.5 Storing Types
        • 10.9.6 Querying
        • 10.9.7 Delete from a Table
        • 10.9.8 Notes & Caveats
        • 10.9.9 DataTypes
        • 10.9.10 External Compatibility
        • 10.9.11 Backwards Compatibility
        • 10.9.12 Performance
        • 10.9.13 Experimental
      • 10.10 SQL Queries
        • 10.10.1 pandas.read_sql_table
        • 10.10.2 pandas.read_sql_query
        • 10.10.3 pandas.read_sql
        • 10.10.4 pandas.DataFrame.to_sql
        • 10.10.5 Writing DataFrames
        • 10.10.6 Reading Tables
        • 10.10.7 Schema support
        • 10.10.8 Querying
        • 10.10.9 Engine connection examples
        • 10.10.10 Advanced SQLAlchemy queries
        • 10.10.11 Sqlite fallback
      • 10.11 Google BigQuery (Experimental)
        • 10.11.1 pandas.io.gbq.read_gbq
        • 10.11.2 pandas.io.gbq.to_gbq
        • 10.11.3 Authentication
        • 10.11.4 Querying
        • 10.11.5 Writing DataFrames
        • 10.11.6 Creating BigQuery Tables
      • 10.12 Stata Format
        • 10.12.1 Writing to Stata format
        • 10.12.2 Reading from Stata format
      • 10.13 SAS Formats
      • 10.14 Other file formats
        • 10.14.1 netCDF
      • 10.15 Performance Considerations
  • Part 2
    • 1 Sparse data structures
      • 1.1 SparseArray
      • 1.2 SparseList
      • 1.3 SparseIndex objects
      • 1.4 Sparse Calculation
      • 1.5 Interaction with scipy.sparse
    • 2 Cookbook
      • 2.1 Idioms
        • 2.1.1 if-then...
        • 2.1.2 Splitting
        • 2.1.3 Building Criteria
      • 2.2 Selection
        • 2.2.1 DataFrames
        • 2.2.2 Panels
        • 2.2.3 New Columns
      • 2.3 MultiIndexing
        • 2.3.1 Arithmetic
        • 2.3.2 Slicing
        • 2.3.3 Sorting
        • 2.3.4 Levels
        • 2.3.5 panelnd
      • 2.4 Missing Data
        • 2.4.1 Replace
      • 2.5 Grouping
        • 2.5.1 Expanding Data
        • 2.5.2 Splitting
        • 2.5.3 Pivot
        • 2.5.4 Apply
      • 2.6 Timeseries
        • 2.6.1 Resampling
      • 2.7 Merge
      • 2.8 Plotting
      • 2.9 Data In/Out
        • 2.9.1 CSV
        • 2.9.2 SQL
        • 2.9.3 Excel
        • 2.9.4 HTML
        • 2.9.5 HDFStore
        • 2.9.6 Binary Files
      • 2.10 Computation
      • 2.11 Timedeltas
      • 2.12 Aliasing Axis Names
      • 2.13 Creating Example Data
    • 3 Computational tools
      • 3.1 Statistical Functions
        • 3.1.1 Percent Change
        • 3.1.2 Covariance
        • 3.1.3 Correlation
        • 3.1.4 Data ranking
      • 3.2 Window Functions
        • 3.2.1 Method Summary
        • 3.2.2 Rolling Windows
        • 3.2.3 Time-aware Rolling
        • 3.2.4 Time-aware Rolling vs. Resampling
        • 3.2.5 Centering Windows
        • 3.2.6 Binary Window Functions
        • 3.2.7 Computing rolling pairwise covariances and correlations
      • 3.3 Aggregation
        • 3.3.1 Applying multiple functions at once
        • 3.3.2 Applying different functions to DataFrame columns
      • 3.4 Expanding Windows
        • 3.4.1 Method Summary
      • 3.5 Exponentially Weighted Windows
    • Time Series / Date functionality
      • 1 Introduction
      • 2 Overview
      • 3 Time Stamps vs. Time Spans
      • 4 Converting to Timestamps
        • 4.1 Invalid Data
        • 4.2 Epoch Timestamps
      • 5 Generating Ranges of Timestamps
      • 6 Timestamp limitations
      • 7 DatetimeIndex
        • 7.1 DatetimeIndex Partial String Indexing
        • 7.2 Datetime Indexing
        • 7.3 Truncating & Fancy Indexing
        • 7.4 Time/Date Components
      • 8 DateOffset objects
        • 8.1 Parametric offsets
        • 8.2 Using offsets with Series / DatetimeIndex
        • 8.3 Custom Business Days (Experimental)
        • 8.4 Business Hour
        • 8.5 Custom Business Hour
        • 8.6 Offset Aliases
        • 8.7 Combining Aliases
        • 8.8 Anchored Offsets
        • 8.9 Anchored Offset Semantics
        • 8.10 Holidays / Holiday Calendars
      • 9 Time series-related instance methods
        • 9.1 Shifting / lagging
        • 9.2 Frequency conversion
        • 9.3 Filling forward / backward
        • 9.4 Converting to Python datetimes
      • 10 Resampling
        • 10.1 Up Sampling
        • 10.2 Sparse Resampling
        • 10.3 Aggregation
      • 11 Time Span Representation
        • 11.1 Period
        • 11.2 PeriodIndex and period_range
        • 11.3 Period Dtypes
        • 11.4 PeriodIndex Partial String Indexing
        • 11.5 Frequency Conversion and Resampling with PeriodIndex
      • 12 Converting between Representations
      • 13 Representing out-of-bounds spans
      • 14 Time Zone Handling
        • 14.1 Working with Time Zones
        • 14.2 Ambiguous Times when Localizing
        • 14.3 TZ aware Dtypes
    • 4 Time Deltas
      • 4.1 Parsing
        • 4.1.1 to_timedelta
        • 4.1.2 Timedelta limitations
      • 4.2 Operations
      • 4.3 Reductions
      • 4.4 Frequency Conversion
      • 4.5 Attributes
      • 4.6 TimedeltaIndex
        • 4.6.1 Using the TimedeltaIndex
        • 4.6.2 Operations
        • 4.6.3 Conversions
      • 4.7 Resampling
    • 5 Categorical Data
      • 5.1 Introduction
      • 5.2 Object Creation
      • 5.3 Description
      • 5.4 Working with categories
        • 5.4.1 Renaming categories
        • 5.4.2 Appending new categories
        • 5.4.3 Removing categories
        • 5.4.4 Removing unused categories
        • 5.4.5 Setting categories
      • 5.5 Sorting and Order
        • 5.5.1 Reordering
        • 5.5.2 Multi Column Sorting
      • 5.6 Comparisons
      • 5.7 Operations
      • 5.8 Data munging
        • 5.8.1 Getting
        • 5.8.2 String and datetime accessors
        • 5.8.3 Setting
        • 5.8.4 Merging
        • 5.8.5 Unioning
      • 5.9 Getting Data In/Out
      • 5.10 Missing Data
      • 5.11 Differences to R’s factor
      • 5.12 Gotchas
        • 5.12.1 Memory Usage
        • 5.12.2 Old style constructor usage
        • 5.12.3 Categorical is not a numpy array
        • 5.12.4 dtype in apply
        • 5.12.5 Categorical Index
        • 5.12.6 Side Effects
    • 6 Remote Data Access
      • 6.1 DataReader
      • 6.2 Google Analytics
        • 6.2.1 Configuring Access to Google Analytics
        • 6.2.2 Using the Google Analytics API
    • 7 Enhancing Performance
      • 7.1 Cython (Writing C extensions for pandas)
        • 7.1.1 Pure python
        • 7.1.2 Plain cython
        • 7.1.3 Adding type
        • 7.1.4 Using ndarray
        • 7.1.5 More advanced techniques
      • 7.2 Using numba
        • 7.2.1 Jit
        • 7.2.2 Vectorize
        • 7.2.3 Caveats
      • 7.3 Expression Evaluation via eval() (Experimental)
        • 7.3.1 Supported Syntax
        • 7.3.2 eval() Examples
        • 7.3.3 The DataFrame.eval method (Experimental)
        • 7.3.4 Local Variables
        • 7.3.5 pandas.eval() Parsers
        • 7.3.6 pandas.eval() Backends
        • 7.3.7 pandas.eval() Performance
        • 7.3.8 Technical Minutia Regarding Expression Evaluation
  • Part 3
    • 1 Caveats and Gotchas
      • 1.1 Using If/Truth Statements with pandas
        • 1.1.1 Bitwise boolean
        • 1.1.2 Using the in operator
      • 1.2 NaN, Integer NA values and NA type promotions
        • 1.2.1 Choice of NA representation
        • 1.2.2 Support for integer NA
        • 1.2.3 NA type promotions
        • 1.2.4 Why not make NumPy like R?
      • 1.3 Integer indexing
      • 1.4 Label-based slicing conventions
        • 1.4.1 Non-monotonic indexes require exact matches
        • 1.4.2 Endpoints are inclusive
      • 1.5 Miscellaneous indexing gotchas
        • 1.5.1 Reindex versus ix gotchas
        • 1.5.2 Reindex potentially changes underlying Series dtype
      • 1.6 Parsing Dates from Text Files
      • 1.7 Differences with NumPy
      • 1.8 Thread-safety
      • 1.9 HTML Table Parsing
      • 1.10 Byte-Ordering Issues
    • 2 rpy2 / R interface
      • 2.1 Updating your code to use rpy2 functions
      • 2.2 R interface with rpy2
      • 2.3 Transferring R data sets into Python
      • 2.4 Converting DataFrames into R objects
      • 2.5 Calling R functions with pandas objects
      • 2.6 High-level interface to R estimators
    • 3 pandas Ecosystem
      • 3.1 Statistics and Machine Learning
        • 3.1.1 Statsmodels
        • 3.1.2 sklearn-pandas
      • 3.2 Visualization
        • 3.2.1 Bokeh
        • 3.2.2 yhat/ggplot
        • 3.2.3 Seaborn
        • 3.2.4 Vincent
        • 3.2.5 IPython Vega
        • 3.2.6 Plotly
        • 3.2.7 Pandas-Qt
      • 3.3 IDE
        • 3.3.1 IPython
        • 3.3.2 quantopian/qgrid
        • 3.3.3 Spyder
      • 3.4 API
        • 3.4.1 pandas-datareader
        • 3.4.2 quandl/Python
        • 3.4.3 pydatastream
        • 3.4.4 pandaSDMX
        • 3.4.5 fredapi
      • 3.5 Domain Specific
        • 3.5.1 Geopandas
        • 3.5.2 xarray
      • 3.6 Out-of-core
        • 3.6.1 Dask
        • 3.6.2 Blaze
        • 3.6.3 Odo
    • 4 Comparison with R / R libraries
      • 4.1 Quick Reference
        • 4.1.1 Querying, Filtering, Sampling
        • 4.1.2 Sorting
        • 4.1.3 Transforming
        • 4.1.4 Grouping and Summarizing
      • 4.2 Base R
        • 4.2.1 Slicing with R’s |c|_
        • 4.2.2 |aggregate|_
        • 4.2.3 |match|_
        • 4.2.4 |tapply|_
        • 4.2.5 |subset|_
        • 4.2.6 |with|_
      • 4.3 plyr
        • 4.3.1 |ddply|_
      • 4.4 reshape / reshape2
        • 4.4.1 melt.array
        • 4.4.2 melt.list
        • 4.4.3 melt.data.frame
        • 4.4.4 cast
        • 4.4.5 factor
    • 5 Comparison with SQL
      • 5.1 SELECT
      • 5.2 WHERE
      • 5.3 GROUP BY
      • 5.4 JOIN
        • 5.4.1 INNER JOIN
        • 5.4.2 LEFT OUTER JOIN
        • 5.4.3 RIGHT JOIN
        • 5.4.4 FULL JOIN
      • 5.5 UNION
      • 5.6 Pandas equivalents for some SQL analytic and aggregate functions
        • 5.6.1 Top N rows with offset
        • 5.6.2 Top N rows per group
      • 5.7 UPDATE
      • 5.8 DELETE
    • 6 Comparison with SAS
      • 6.1 Data Structures
        • 6.1.1 General Terminology Translation
        • 6.1.2 DataFrame / Series
        • 6.1.3 Index
      • 6.2 Data Input / Output
        • 6.2.1 Constructing a DataFrame from Values
        • 6.2.2 Reading External Data
        • 6.2.3 Exporting Data
      • 6.3 Data Operations
        • 6.3.1 Operations on Columns
        • 6.3.2 Filtering
        • 6.3.3 If/Then Logic
        • 6.3.4 Date Functionality
        • 6.3.5 Selection of Columns
        • 6.3.6 Sorting by Values
      • 6.4 Merging
      • 6.5 Missing Data
      • 6.6 GroupBy
        • 6.6.1 Aggregation
        • 6.6.2 Transformation
        • 6.6.3 By Group Processing
      • 6.7 Other Considerations
        • 6.7.1 Disk vs Memory
        • 6.7.2 Data Interop
    • 7 Internals
      • 7.1 Indexing
        • 7.1.1 MultiIndex
      • 7.2 Subclassing pandas Data Structures
        • 7.2.1 Override Constructor Properties
        • 7.2.2 Define Original Properties
Pandas Doc
  • Docs »
  • Overview: module code

All modules for which code is available

  • jinja2.environment
  • pandas.computation.eval
  • pandas.core.algorithms
  • pandas.core.base
  • pandas.core.categorical
  • pandas.core.config
  • pandas.core.frame
  • pandas.core.generic
  • pandas.core.groupby
  • pandas.core.ops
  • pandas.core.panel
  • pandas.core.panelnd
  • pandas.core.reshape
  • pandas.core.series
  • pandas.core.strings
  • pandas.core.window
  • pandas.formats.style
  • pandas.indexes.base
  • pandas.indexes.category
  • pandas.indexes.multi
  • pandas.io.clipboard
  • pandas.io.excel
  • pandas.io.gbq
  • pandas.io.html
  • pandas.io.json
  • pandas.io.parsers
  • pandas.io.pickle
  • pandas.io.pytables
  • pandas.io.sas.sasreader
  • pandas.io.sql
  • pandas.io.stata
  • pandas.sparse.series
  • pandas.tools.merge
  • pandas.tools.pivot
  • pandas.tools.plotting
  • pandas.tools.tile
  • pandas.tools.util
  • pandas.tseries.base
  • pandas.tseries.common
  • pandas.tseries.frequencies
  • pandas.tseries.index
  • pandas.tseries.period
  • pandas.tseries.resample
  • pandas.tseries.tdi
  • pandas.tseries.timedeltas
  • pandas.tseries.tools
  • pandas.types.missing
  • pandas.util.decorators
  • pandas.util.nosetester

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