pandas.DataFrame

class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)

Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure

Parameters:

data : numpy ndarray (structured or homogeneous), dict, or DataFrame

Dict can contain Series, arrays, constants, or list-like objects

index : Index or array-like

Index to use for resulting frame. Will default to np.arange(n) if no indexing information part of input data and no index provided

columns : Index or array-like

Column labels to use for resulting frame. Will default to np.arange(n) if no column labels are provided

dtype : dtype, default None

Data type to force. Only a single dtype is allowed. If None, infer

copy : boolean, default False

Copy data from inputs. Only affects DataFrame / 2d ndarray input

See also

DataFrame.from_records

constructor from tuples, also record arrays

DataFrame.from_dict

from dicts of Series, arrays, or dicts

DataFrame.from_items

from sequence of (key, value) pairs

pandas.read_csv, pandas.read_table, pandas.read_clipboard

Examples

Constructing DataFrame from a dictionary.

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)),
...                    columns=['a', 'b', 'c', 'd', 'e'])
>>> df2
    a   b   c   d   e
0   2   8   8   3   4
1   4   2   9   0   9
2   1   0   7   8   0
3   5   1   7   1   3
4   6   0   2   4   2

Attributes

T	Transpose index and columns
at	Fast label-based scalar accessor
axes	Return a list with the row axis labels and column axis labels as the only members.
blocks	Internal property, property synonym for as_blocks()
dtypes	Return the dtypes in this object.
empty	True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0.
ftypes	Return the ftypes (indication of sparse/dense and dtype) in this object.
iat	Fast integer location scalar accessor.
iloc	Purely integer-location based indexing for selection by position.
is_copy
ix	A primarily label-location based indexer, with integer position fallback.
loc	Purely label-location based indexer for selection by label.
ndim	Number of axes / array dimensions
shape	Return a tuple representing the dimensionality of the DataFrame.
size	number of elements in the NDFrame
style	Property returning a Styler object containing methods for building a styled HTML representation fo the DataFrame.
values	Numpy representation of NDFrame

Methods

abs()	Return an object with absolute value taken–only applicable to objects that are all numeric.
add(other[, axis, level, fill_value])	Addition of dataframe and other, element-wise (binary operator add).
add_prefix(prefix)	Concatenate prefix string with panel items names.
add_suffix(suffix)	Concatenate suffix string with panel items names.
agg(func[, axis])	Aggregate using callable, string, dict, or list of string/callables
aggregate(func[, axis])	Aggregate using callable, string, dict, or list of string/callables
align(other[, join, axis, level, copy, ...])	Align two objects on their axes with the
all([axis, bool_only, skipna, level])	Return whether all elements are True over requested axis
any([axis, bool_only, skipna, level])	Return whether any element is True over requested axis
append(other[, ignore_index, verify_integrity])	Append rows of other to the end of this frame, returning a new object.
apply(func[, axis, broadcast, raw, reduce, args])	Applies function along input axis of DataFrame.
applymap(func)	Apply a function to a DataFrame that is intended to operate elementwise, i.e.
as_blocks([copy])	Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
as_matrix([columns])	Convert the frame to its Numpy-array representation.
asfreq(freq[, method, how, normalize, ...])	Convert TimeSeries to specified frequency.
asof(where[, subset])	The last row without any NaN is taken (or the last row without
assign(**kwargs)	Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones.
astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
at_time(time[, asof])	Select values at particular time of day (e.g.
between_time(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna(method='bfill')
bool()	Return the bool of a single element PandasObject.
boxplot([column, by, ax, fontsize, rot, ...])	Make a box plot from DataFrame column optionally grouped by some columns or
clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
clip_lower(threshold[, axis, inplace])	Return copy of the input with values below given value(s) truncated.
clip_upper(threshold[, axis, inplace])	Return copy of input with values above given value(s) truncated.
combine(other, func[, fill_value, overwrite])	Add two DataFrame objects and do not propagate NaN values, so if for a
combine_first(other)	Combine two DataFrame objects and default to non-null values in frame calling the method.
compound([axis, skipna, level])	Return the compound percentage of the values for the requested axis
consolidate([inplace])	DEPRECATED: consolidate will be an internal implementation only.
convert_objects([convert_dates, ...])	Deprecated.
copy([deep])	Make a copy of this objects data.
corr([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values
corrwith(other[, axis, drop])	Compute pairwise correlation between rows or columns of two DataFrame objects.
count([axis, level, numeric_only])	Return Series with number of non-NA/null observations over requested axis.
cov([min_periods])	Compute pairwise covariance of columns, excluding NA/null values
cummax([axis, skipna])	Return cumulative max over requested axis.
cummin([axis, skipna])	Return cumulative minimum over requested axis.
cumprod([axis, skipna])	Return cumulative product over requested axis.
cumsum([axis, skipna])	Return cumulative sum over requested axis.
describe([percentiles, include, exclude])	Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
diff([periods, axis])	1st discrete difference of object
div(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator truediv).
divide(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator truediv).
dot(other)	Matrix multiplication with DataFrame or Series objects
drop([labels, axis, index, columns, level, ...])	Return new object with labels in requested axis removed.
drop_duplicates([subset, keep, inplace])	Return DataFrame with duplicate rows removed, optionally only
dropna([axis, how, thresh, subset, inplace])	Return object with labels on given axis omitted where alternately any
duplicated([subset, keep])	Return boolean Series denoting duplicate rows, optionally only
eq(other[, axis, level])	Wrapper for flexible comparison methods eq
equals(other)	Determines if two NDFrame objects contain the same elements.
eval(expr[, inplace])	Evaluate an expression in the context of the calling DataFrame instance.
ewm([com, span, halflife, alpha, ...])	Provides exponential weighted functions
expanding([min_periods, freq, center, axis])	Provides expanding transformations.
ffill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna(method='ffill')
fillna([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method
filter([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.
first(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
first_valid_index()	Return index for first non-NA/null value.
floordiv(other[, axis, level, fill_value])	Integer division of dataframe and other, element-wise (binary operator floordiv).
from_csv(path[, header, sep, index_col, ...])	Read CSV file (DEPRECATED, please use pandas.read_csv() instead).
from_dict(data[, orient, dtype])	Construct DataFrame from dict of array-like or dicts
from_items(items[, columns, orient])	Convert (key, value) pairs to DataFrame.
from_records(data[, index, exclude, ...])	Convert structured or record ndarray to DataFrame
ge(other[, axis, level])	Wrapper for flexible comparison methods ge
get(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
get_dtype_counts()	Return the counts of dtypes in this object.
get_ftype_counts()	Return the counts of ftypes in this object.
get_value(index, col[, takeable])	Quickly retrieve single value at passed column and index
get_values()	same as values (but handles sparseness conversions)
groupby([by, axis, level, as_index, sort, ...])	Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.
gt(other[, axis, level])	Wrapper for flexible comparison methods gt
head([n])	Return the first n rows.
hist(data[, column, by, grid, xlabelsize, ...])	Draw histogram of the DataFrame’s series using matplotlib / pylab.
idxmax([axis, skipna])	Return index of first occurrence of maximum over requested axis.
idxmin([axis, skipna])	Return index of first occurrence of minimum over requested axis.
infer_objects()	Attempt to infer better dtypes for object columns.
info([verbose, buf, max_cols, memory_usage, ...])	Concise summary of a DataFrame.
insert(loc, column, value[, allow_duplicates])	Insert column into DataFrame at specified location.
interpolate([method, axis, limit, inplace, ...])	Interpolate values according to different methods.
isin(values)	Return boolean DataFrame showing whether each element in the DataFrame is contained in values.
isna()	Return a boolean same-sized object indicating if the values are NA.
isnull()	Return a boolean same-sized object indicating if the values are NA.
items()	Iterator over (column name, Series) pairs.
iteritems()	Iterator over (column name, Series) pairs.
iterrows()	Iterate over DataFrame rows as (index, Series) pairs.
itertuples([index, name])	Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.
join(other[, on, how, lsuffix, rsuffix, sort])	Join columns with other DataFrame either on index or on a key column.
keys()	Get the ‘info axis’ (see Indexing for more)
kurt([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
kurtosis([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
last(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
last_valid_index()	Return index for last non-NA/null value.
le(other[, axis, level])	Wrapper for flexible comparison methods le
lookup(row_labels, col_labels)	Label-based “fancy indexing” function for DataFrame.
lt(other[, axis, level])	Wrapper for flexible comparison methods lt
mad([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
mask(cond[, other, inplace, axis, level, ...])	Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
max([axis, skipna, level, numeric_only])	This method returns the maximum of the values in the object.
mean([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis
median([axis, skipna, level, numeric_only])	Return the median of the values for the requested axis
melt([id_vars, value_vars, var_name, ...])	“Unpivots” a DataFrame from wide format to long format, optionally
memory_usage([index, deep])	Memory usage of DataFrame columns.
merge(right[, how, on, left_on, right_on, ...])	Merge DataFrame objects by performing a database-style join operation by columns or indexes.
min([axis, skipna, level, numeric_only])	This method returns the minimum of the values in the object.
mod(other[, axis, level, fill_value])	Modulo of dataframe and other, element-wise (binary operator mod).
mode([axis, numeric_only])	Gets the mode(s) of each element along the axis selected.
mul(other[, axis, level, fill_value])	Multiplication of dataframe and other, element-wise (binary operator mul).
multiply(other[, axis, level, fill_value])	Multiplication of dataframe and other, element-wise (binary operator mul).
ne(other[, axis, level])	Wrapper for flexible comparison methods ne
nlargest(n, columns[, keep])	Get the rows of a DataFrame sorted by the n largest values of columns.
notna()	Return a boolean same-sized object indicating if the values are not NA.
notnull()	Return a boolean same-sized object indicating if the values are not NA.
nsmallest(n, columns[, keep])	Get the rows of a DataFrame sorted by the n smallest values of columns.
nunique([axis, dropna])	Return Series with number of distinct observations over requested axis.
pct_change([periods, fill_method, limit, freq])	Percent change over given number of periods.
pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
pivot([index, columns, values])	Reshape data (produce a “pivot” table) based on column values.
pivot_table([values, index, columns, ...])	Create a spreadsheet-style pivot table as a DataFrame.
plot	alias of FramePlotMethods
pop(item)	Return item and drop from frame.
pow(other[, axis, level, fill_value])	Exponential power of dataframe and other, element-wise (binary operator pow).
prod([axis, skipna, level, numeric_only, ...])	Return the product of the values for the requested axis
product([axis, skipna, level, numeric_only, ...])	Return the product of the values for the requested axis
quantile([q, axis, numeric_only, interpolation])	Return values at the given quantile over requested axis, a la numpy.percentile.
query(expr[, inplace])	Query the columns of a frame with a boolean expression.
radd(other[, axis, level, fill_value])	Addition of dataframe and other, element-wise (binary operator radd).
rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
rdiv(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator rtruediv).
reindex([labels, index, columns, axis, ...])	Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_axis(labels[, axis, method, level, ...])	Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
reindex_like(other[, method, copy, limit, ...])	Return an object with matching indices to myself.
rename([mapper, index, columns, axis, copy, ...])	Alter axes labels.
rename_axis(mapper[, axis, copy, inplace])	Alter the name of the index or columns.
reorder_levels(order[, axis])	Rearrange index levels using input order.
replace([to_replace, value, inplace, limit, ...])	Replace values given in ‘to_replace’ with ‘value’.
resample(rule[, how, axis, fill_method, ...])	Convenience method for frequency conversion and resampling of time series.
reset_index([level, drop, inplace, ...])	For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc.
rfloordiv(other[, axis, level, fill_value])	Integer division of dataframe and other, element-wise (binary operator rfloordiv).
rmod(other[, axis, level, fill_value])	Modulo of dataframe and other, element-wise (binary operator rmod).
rmul(other[, axis, level, fill_value])	Multiplication of dataframe and other, element-wise (binary operator rmul).
rolling(window[, min_periods, freq, center, ...])	Provides rolling window calculations.
round([decimals])	Round a DataFrame to a variable number of decimal places.
rpow(other[, axis, level, fill_value])	Exponential power of dataframe and other, element-wise (binary operator rpow).
rsub(other[, axis, level, fill_value])	Subtraction of dataframe and other, element-wise (binary operator rsub).
rtruediv(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator rtruediv).
sample([n, frac, replace, weights, ...])	Returns a random sample of items from an axis of object.
select(crit[, axis])	Return data corresponding to axis labels matching criteria
select_dtypes([include, exclude])	Return a subset of a DataFrame including/excluding columns based on their dtype.
sem([axis, skipna, level, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
set_axis(labels[, axis, inplace])	Assign desired index to given axis
set_index(keys[, drop, append, inplace, ...])	Set the DataFrame index (row labels) using one or more existing columns.
set_value(index, col, value[, takeable])	Put single value at passed column and index
shift([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis
slice_shift([periods, axis])	Equivalent to shift without copying data.
sort_index([axis, level, ascending, ...])	Sort object by labels (along an axis)
sort_values(by[, axis, ascending, inplace, ...])	Sort by the values along either axis
sortlevel([level, axis, ascending, inplace, ...])	DEPRECATED: use DataFrame.sort_index()
squeeze([axis])	Squeeze length 1 dimensions.
stack([level, dropna])	Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels.
std([axis, skipna, level, ddof, numeric_only])	Return sample standard deviation over requested axis.
sub(other[, axis, level, fill_value])	Subtraction of dataframe and other, element-wise (binary operator sub).
subtract(other[, axis, level, fill_value])	Subtraction of dataframe and other, element-wise (binary operator sub).
sum([axis, skipna, level, numeric_only, ...])	Return the sum of the values for the requested axis
swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately
swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex on a particular axis
tail([n])	Return the last n rows.
take(indices[, axis, convert, is_copy])	Return the elements in the given positional indices along an axis.
to_clipboard([excel, sep])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.
to_csv([path_or_buf, sep, na_rep, ...])	Write DataFrame to a comma-separated values (csv) file
to_dense()	Return dense representation of NDFrame (as opposed to sparse)
to_dict([orient, into])	Convert DataFrame to dictionary.
to_excel(excel_writer[, sheet_name, na_rep, ...])	Write DataFrame to an excel sheet
to_feather(fname)	write out the binary feather-format for DataFrames
to_gbq(destination_table, project_id[, ...])	Write a DataFrame to a Google BigQuery table.
to_hdf(path_or_buf, key, **kwargs)	Write the contained data to an HDF5 file using HDFStore.
to_html([buf, columns, col_space, header, ...])	Render a DataFrame as an HTML table.
to_json([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
to_latex([buf, columns, col_space, header, ...])	Render an object to a tabular environment table.
to_msgpack([path_or_buf, encoding])	msgpack (serialize) object to input file path
to_panel()	Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
to_parquet(fname[, engine, compression])	Write a DataFrame to the binary parquet format.
to_period([freq, axis, copy])	Convert DataFrame from DatetimeIndex to PeriodIndex with desired
to_pickle(path[, compression, protocol])	Pickle (serialize) object to input file path.
to_records([index, convert_datetime64])	Convert DataFrame to record array.
to_sparse([fill_value, kind])	Convert to SparseDataFrame
to_sql(name, con[, flavor, schema, ...])	Write records stored in a DataFrame to a SQL database.
to_stata(fname[, convert_dates, ...])	A class for writing Stata binary dta files from array-like objects
to_string([buf, columns, col_space, header, ...])	Render a DataFrame to a console-friendly tabular output.
to_timestamp([freq, how, axis, copy])	Cast to DatetimeIndex of timestamps, at beginning of period
to_xarray()	Return an xarray object from the pandas object.
transform(func, *args, **kwargs)	Call function producing a like-indexed NDFrame
transpose(*args, **kwargs)	Transpose index and columns
truediv(other[, axis, level, fill_value])	Floating division of dataframe and other, element-wise (binary operator truediv).
truncate([before, after, axis, copy])	Truncates a sorted DataFrame/Series before and/or after some particular index value.
tshift([periods, freq, axis])	Shift the time index, using the index’s frequency if available.
tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
tz_localize(tz[, axis, level, copy, ambiguous])	Localize tz-naive TimeSeries to target time zone.
unstack([level, fill_value])	Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
update(other[, join, overwrite, ...])	Modify DataFrame in place using non-NA values from passed DataFrame.
var([axis, skipna, level, ddof, numeric_only])	Return unbiased variance over requested axis.
where(cond[, other, inplace, axis, level, ...])	Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
xs(key[, axis, level, drop_level])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.