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, otherwise 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_csv
from CSV files
DataFrame.from_items
from sequence of (key, value) pairs

pandas.read_csv, pandas.read_table, pandas.read_clipboard

Examples

>>> d = {'col1': ts1, 'col2': ts2}
>>> df = DataFrame(data=d, index=index)
>>> df2 = DataFrame(np.random.randn(10, 5))
>>> df3 = DataFrame(np.random.randn(10, 5),
...                 columns=['a', 'b', 'c', 'd', 'e'])

Attributes

T Transpose index and columns
at Fast label-based scalar accessor
axes
blocks Internal property, property synonym for as_blocks()
dtypes Return the dtypes in this object
empty True if NDFrame is entirely empty [no items]
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.
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
size number of elements in the NDFrame
values Numpy representation of NDFrame
is_copy  

Methods

abs() Return an object with absolute value taken.
add(other[, axis, level, fill_value]) Binary operator add with support to substitute a fill_value for missing data in
add_prefix(prefix) Concatenate prefix string with panel items names.
add_suffix(suffix) Concatenate suffix string with panel items names
align(other[, join, axis, level, copy, ...]) Align two object 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() 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 all TimeSeries inside to specified frequency using DateOffset objects.
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, raise_on_error]) Cast object to input numpy.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 NDFrame.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, out, axis]) Trim values at input threshold(s)
clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated
clip_upper(threshold[, axis]) 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
combineAdd(other) Add two DataFrame objects and do not propagate
combineMult(other) Multiply two DataFrame objects and do not propagate NaN values, so if
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]) Compute NDFrame with “consolidated” internals (data of each dtype grouped together in a single ndarray).
convert_objects([convert_dates, ...]) Attempt to infer better dtype for object columns
copy([deep]) Make a copy of this object
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, dtype, out, skipna]) Return cumulative max over requested axis.
cummin([axis, dtype, out, skipna]) Return cumulative min over requested axis.
cumprod([axis, dtype, out, skipna]) Return cumulative prod over requested axis.
cumsum([axis, dtype, out, skipna]) Return cumulative sum over requested axis.
describe([percentile_width, percentiles, ...]) Generate various summary statistics, excluding NaN values.
diff([periods, axis]) 1st discrete difference of object
div(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
divide(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
dot(other) Matrix multiplication with DataFrame or Series objects
drop(labels[, axis, level, inplace, errors]) Return new object with labels in requested axis removed
drop_duplicates(*args, **kwargs) 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(*args, **kwargs) 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, **kwargs) Evaluate an expression in the context of the calling DataFrame instance.
ffill([axis, inplace, limit, downcast]) Synonym for NDFrame.fillna(method=’ffill’)
fillna([value, method, axis, inplace, ...]) Fill NA/NaN values using the specified method
filter([items, like, regex, axis]) Restrict the info axis to set of items or wildcard
first(offset) Convenience method for subsetting initial periods of time series data
first_valid_index() Return label for first non-NA/null value
floordiv(other[, axis, level, fill_value]) Binary operator floordiv with support to substitute a fill_value for missing data in
from_csv(path[, header, sep, index_col, ...]) Read delimited file into DataFrame
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
gt(other[, axis, level]) Wrapper for flexible comparison methods gt
head([n]) Returns first n rows
hist(data[, column, by, grid, xlabelsize, ...]) Draw histogram of the DataFrame’s series using matplotlib / pylab.
icol(i)
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.
iget_value(i, j)
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.
irow(i[, copy])
isin(values) Return boolean DataFrame showing whether each element in the DataFrame is contained in values.
isnull() Return a boolean same-sized object indicating if the values are null
iteritems() Iterator over (column, series) pairs
iterkv(*args, **kwargs) iteritems alias used to get around 2to3. Deprecated
iterrows() Iterate over rows of DataFrame as (index, Series) pairs.
itertuples([index]) Iterate over rows of DataFrame as tuples, with index value
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 Fishers definition of kurtosis (kurtosis of normal == 0.0).
kurtosis([axis, skipna, level, numeric_only]) Return unbiased kurtosis over requested axis using Fishers definition of kurtosis (kurtosis of normal == 0.0).
last(offset) Convenience method for subsetting final periods of time series data
last_valid_index() Return label for last non-NA/null value
le(other[, axis, level]) Wrapper for flexible comparison methods le
load(path) Deprecated.
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
memory_usage([index]) 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]) Binary operator mod with support to substitute a fill_value for missing data in
mode([axis, numeric_only]) Gets the mode(s) of each element along the axis selected.
mul(other[, axis, level, fill_value]) Binary operator mul with support to substitute a fill_value for missing data in
multiply(other[, axis, level, fill_value]) Binary operator mul with support to substitute a fill_value for missing data in
ne(other[, axis, level]) Wrapper for flexible comparison methods ne
notnull() Return a boolean same-sized object indicating if the values are
pct_change([periods, fill_method, limit, freq]) Percent change over given number of periods.
pivot([index, columns, values]) Reshape data (produce a “pivot” table) based on column values.
pivot_table(data[, values, index, columns, ...]) Create a spreadsheet-style pivot table as a DataFrame.
plot(data[, x, y, kind, ax, subplots, ...]) Make plots of DataFrame using matplotlib / pylab.
pop(item) Return item and drop from frame.
pow(other[, axis, level, fill_value]) Binary operator pow with support to substitute a fill_value for missing data in
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]) Return values at the given quantile over requested axis, a la numpy.percentile.
query(expr, **kwargs) Query the columns of a frame with a boolean expression.
radd(other[, axis, level, fill_value]) Binary operator radd with support to substitute a fill_value for missing data in
rank([axis, numeric_only, method, ...]) Compute numerical data ranks (1 through n) along axis.
rdiv(other[, axis, level, fill_value]) Binary operator rtruediv with support to substitute a fill_value for missing data in
reindex([index, columns]) 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 indicies to myself
rename([index, columns]) Alter axes input function or functions.
rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions.
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 regular time-series data.
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]) Binary operator rfloordiv with support to substitute a fill_value for missing data in
rmod(other[, axis, level, fill_value]) Binary operator rmod with support to substitute a fill_value for missing data in
rmul(other[, axis, level, fill_value]) Binary operator rmul with support to substitute a fill_value for missing data in
rpow(other[, axis, level, fill_value]) Binary operator rpow with support to substitute a fill_value for missing data in
rsub(other[, axis, level, fill_value]) Binary operator rsub with support to substitute a fill_value for missing data in
rtruediv(other[, axis, level, fill_value]) Binary operator rtruediv with support to substitute a fill_value for missing data in
sample([n, frac, replace, weights, ...]) Returns a random sample of items from an axis of object.
save(path) Deprecated.
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(axis, labels) public verson of axis assignment
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([columns, axis, ascending, inplace, ...]) Sort DataFrame either by labels (along either axis) or by the values in
sort_index([axis, by, ascending, inplace, ...]) Sort DataFrame either by labels (along either axis) or by the values in
sortlevel([level, axis, ascending, inplace, ...]) Sort multilevel index by chosen axis and primary level.
squeeze() 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 unbiased standard deviation over requested axis.
sub(other[, axis, level, fill_value]) Binary operator sub with support to substitute a fill_value for missing data in
subtract(other[, axis, level, fill_value]) Binary operator sub with support to substitute a fill_value for missing data in
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]) Returns last n rows
take(indices[, axis, convert, is_copy]) Analogous to ndarray.take
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(*args, **kwargs) Convert DataFrame to dictionary.
to_excel(excel_writer[, sheet_name, na_rep, ...]) Write DataFrame to a excel sheet
to_gbq(destination_table[, project_id, ...]) Write a DataFrame to a Google BigQuery table.
to_hdf(path_or_buf, key, **kwargs) activate the HDFStore
to_html([buf, columns, col_space, colSpace, ...]) 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, ...]) Render a DataFrame to a tabular environment table.
to_msgpack([path_or_buf]) msgpack (serialize) object to input file path
to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
to_period([freq, axis, copy]) Convert DataFrame from DatetimeIndex to PeriodIndex with desired
to_pickle(path) 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, ...]) 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_wide(*args, **kwargs)
transpose() Transpose index and columns
truediv(other[, axis, level, fill_value]) Binary operator truediv with support to substitute a fill_value for missing data in
truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular dates.
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(*args, **kwargs) Localize tz-naive TimeSeries to target time zone
unstack([level]) 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, copy, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.