|  add(self, other, axis='columns', level=None, fill_value=None)

 |      Addition of dataframe and other, element-wise (binary operator `add`).

 |

 |      Equivalent to ``dataframe + other``, but with support to substitute a fill_value for

 |      missing data in one of the inputs.

 |

 |      Parameters

 |      ----------

 |      other : Series, DataFrame, or constant

 |      axis : {0, 1, 'index', 'columns'}

 |          For Series input, axis to match Series index on

 |      level : int or name

 |          Broadcast across a level, matching Index values on the

 |          passed MultiIndex level

 |      fill_value : None or float value, default None

 |          Fill existing missing (NaN) values, and any new element needed for

 |          successful DataFrame alignment, with this value before computation.

 |          If data in both corresponding DataFrame locations is missing

 |          the result will be missing

pandas.DataFrame.add方法

aggregate(self, func, axis=0, *args, **kwargs)

 |      Aggregate using one or more operations over the specified axis.

 |

 |      .. versionadded:: 0.20.0

 |

 |      Parameters

 |      ----------

 |      func : function, string, dictionary, or list of string/functions

 |          Function to use for aggregating the data. If a function, must either

 |          work when passed a DataFrame or when passed to DataFrame.apply. For

 |          a DataFrame, can pass a dict, if the keys are DataFrame column names.

 |

 |          Accepted combinations are:

 |

 |          - string function name.

 |          - function.

 |          - list of functions.

 |          - dict of column names -> functions (or list of functions).

pandas.DataFrame.aggregate方法

mad(self, axis=None, skipna=None, level=None)

    Return the mean absolute deviation of the values for the requested axis

max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

    This method returns the maximum of the values in the object.If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.

mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

    Return the mean of the values for the requested axis

median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

    Return the median of the values for the requested axis

min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

    This method returns the minimum of the values in the object.

memory_usage(self, index=True, deep=False)

    Return the memory usage of each column in bytes.

merge(self, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

    Merge DataFrame objects by performing a database-style join operation by columns or indexes.

align(self, other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None):

    Align two objects on their axes with the specified join method for each axis Index

all(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs):

    Return whether all elements are True over series or dataframe axis.

any(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs):

    Return whether any element is True over requested axis.

apply(self, func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds):

    Apply a function along an axis of the DataFrame.

applymap(self, func):

    Apply a function to a Dataframe elementwise.This method applies a function that accepts and returns a scalarto every element of a DataFrame.

append(self, other, ignore_index=False, verify_integrity=False, sort=None):

    Append rows of `other` to the end of this frame, returning a new object. Columns not in this frame are added as new columns.

assign(self, **kwargs):

    Assign new columns to a DataFrame, returning a new object(a copy) with the new columns added to the original ones.Existing columns that are re-assigned will be overwritten.

insert(self, loc, column, value, allow_duplicates=False)

    Insert column into DataFrame at specified location.    

combine(self, other, func, fill_value=None, overwrite=True):

    Add two DataFrame objects and do not propagate NaN values, so if for a(column, time) one frame is missing a value, it will default to theother frame's value (which might be NaN as well)

count(self, axis=0, level=None, numeric_only=False):

    Count non-NA cells for each column or row.

cov(self, min_periods=None):

   Compute pairwise covariance of columns, excluding NA/null values.

drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise'):

    Drop specified labels from rows or columns.

drop_duplicates(self, subset=None, keep='first', inplace=False):

    Return DataFrame with duplicate rows removed, optionally onlyconsidering certain columns

dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False)

    Remove missing values.

duplicated(self, subset=None, keep='first')

    Return boolean Series denoting duplicate rows, optionally onlyconsidering certain columns

eq(self, other, axis='columns', level=None)

    Wrapper for flexible comparison methods eq

eval(self, expr, inplace=False, **kwargs)

    Evaluate a string describing operations on DataFrame columns.

fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

    Fill NA/NaN values using the specified method

ge(self, other, axis='columns', level=None)

    Wrapper for flexible comparison methods ge

gt(self, other, axis='columns', level=None)

    Wrapper for flexible comparison methods gt

le(self, other, axis='columns', level=None)

    Wrapper for flexible comparison methods le

lt(self, other, axis='columns', level=None)

    Wrapper for flexible comparison methods lt

get_value(self, index, col, takeable=False)

    Quickly retrieve single value at passed column and index

info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)

    Print a concise summary of a DataFrame.

isin(self, values)

    Return boolean DataFrame showing whether each element in theDataFrame is contained in values.

isna(self)

    Detect missing values.Return a boolean same-sized object indicating if the values are NA.

isnull(self)

    Detect missing values.Return a boolean same-sized object indicating if the values are NA.

iteritems(self)

    Iterator over (column name, Series) pairs.

iterrows(self)

    Iterate over DataFrame rows as (index, Series) pairs.

itertuples(self, index=True, name='Pandas')

    Iterate over DataFrame rows as namedtuples, with index value as firstelement of the tuple.

join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

    Join columns with other DataFrame either on index or on a keycolumn. Efficiently Join multiple DataFrame objects by index at once bypassing a list.