Class DataFrame (0.12.0)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains 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.

Access a single value for a row/column label pair.

Return a list representing the axes of the DataFrame.

It has the row axis labels and column axis labels as the only members. They are returned in that order.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes[1:]
[Index(['col1', 'col2'], dtype='object')]

The column labels of the DataFrame.

Return the dtypes in the DataFrame.

This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types aren't supported yet in BigQuery DataFrames.

Indicates whether Series/DataFrame is empty.

True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0.

Access a single value for a row/column pair by integer position.

Purely integer-location based indexing for selection by position.

The index (row labels) of the DataFrame.

The index of a DataFrame is a series of labels that identify each row. The labels can be integers, strings, or any other hashable type. The index is used for label-based access and alignment, and can be accessed or modified using this attribute.

Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

• A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
• A list of labels, e.g. ['a', 'b', 'c'].
• A boolean series of the same length as the axis being sliced, e.g. [True, False, True].
• An alignable Index. The index of the returned selection will be the input.
• Not supported yet An alignable boolean Series. The index of the key will be aligned before masking.
• Not supported yet A slice object with labels, e.g. 'a':'f'. Note: contrary to usual python slices, both the start and the stop are included.
• Not supported yet A callable function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).

Return an int representing the number of axes / array dimensions.

BigQuery job metadata for the most recent query.

Return a tuple representing the dimensionality of the DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'col1': [1, 2, 3],
...                     'col2': [4, 5, 6]})
>>> df.shape
(3, 2)

Return an int representing the number of elements in this object.

Compiles this DataFrame's expression tree to SQL.

Return the values of DataFrame in the form of a NumPy array.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.values
array([[1, 3],
       [2, 4]], dtype=object)

Used to support numpy ufuncs. See: https://numpy.org/doc/stable/reference/ufuncs.html

Gets the specified column(s) from the DataFrame.

Converts a DataFrame to a string. Calls to_pandas.

Only represents the first <xref uid="bigframes.options">bigframes.options</xref>.display.max_rows.

Modify or insert a column into the DataFrame.

Note: This does not modify the original table the DataFrame was derived from.

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Get addition of DataFrame and other, element-wise (binary operator +).

Equivalent to dataframe + other. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].add(df['B'])
0    5
1    7
2    9
dtype: Int64

You can also use arithmetic operator +:

>>> df['A'] + (df['B'])
0    5
1    7
2    9
dtype: Int64

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Aggregate using one or more operations over the specified axis.

Aggregate using one or more operations over the specified axis.

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a Series or along a DataFrame axis that is False or equivalent (e.g. zero or empty).

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is the DataFrame's index (axis=0) the final return type is inferred from the return type of the applied function.

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Cast a pandas object to a specified dtype dtype.

Fill NA/NaN values by using the next valid observation to fill the gap.

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df1 = bpd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3
<BLANKLINE>
[2 rows x 2 columns]

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the 'first' dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df1 = bpd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0
<BLANKLINE>
[2 rows x 2 columns]

Make a copy of this object's indices and data.

A new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object.

Count non-NA cells for each column or row.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Return cumulative maximum over a DataFrame axis.

Returns a DataFrame of the same size containing the cumulative maximum.

Return cumulative minimum over a DataFrame axis.

Returns a DataFrame of the same size containing the cumulative minimum.

Return cumulative product over a DataFrame axis.

Returns a DataFrame of the same size containing the cumulative product.

Return cumulative sum over a DataFrame axis.

Returns a DataFrame of the same size containing the cumulative sum.

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.

Only supports numeric columns.

First discrete difference of element.

Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to dataframe / other. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].truediv(df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to dataframe / other. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].truediv(df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series or DataFrame.

It can also be called using self @ other.

Drop specified labels from columns.

Remove columns by directly specifying column names.

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Return DataFrame with requested index / column level(s) removed.

Remove missing values.

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Get equal to of DataFrame and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].eq(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

You can also use arithmetic operator ==:

df["degrees"] == 360 circle True triangle False rectangle True Name: degrees, dtype: boolean

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.

Provide expanding window calculations.

Fill NA/NaN values by propagating the last valid observation to next valid.

Fill NA/NaN values using the specified method.

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

API documentation for first_valid_index method.

Get integer division of DataFrame and other, element-wise (binary operator //).

Equivalent to dataframe // other. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].floordiv(df['B'])
0    0
1    0
2    0
dtype: Int64

You can also use arithmetic operator //:

>>> df['A'] // (df['B'])
0    0
1    0
2    0
dtype: Int64

Get 'greater than or equal to' of DataFrame and other, element-wise (binary operator >=).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ge(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

You can also use arithmetic operator >=:

>>> df["degrees"] >= 360
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Group DataFrame by columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Get 'greater than' of DataFrame and other, element-wise (binary operator >).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].gt(360)
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator >:

>>> df["degrees"] > 360
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

Not yet supported For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n].

If n is larger than the number of rows, this function returns all rows.

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Whether each element in the DataFrame is contained in values.

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values get mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values get mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Join columns of another DataFrame.

Join columns with other DataFrame on index

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Get 'less than or equal to' of dataframe and other, element-wise (binary operator <=).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].le(180)
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator <=:

>>> df["degrees"] <= 180
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

Get 'less than' of DataFrame and other, element-wise (binary operator <).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].lt(180)
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator <:

>>> df["degrees"] < 180
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Return the mean of the values over the requested axis.

Return the median of the values over the requested axis.

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.

Merge DataFrame objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Get modulo of DataFrame and other, element-wise (binary operator %).

Equivalent to dataframe % other. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mod(df['B'])
0    1
1    2
2    3
dtype: Int64

You can also use arithmetic operator %:

>>> df['A'] % (df['B'])
0    1
1    2
2    3
dtype: Int64

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to dataframe * other. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to dataframe * other. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Get not equal to of DataFrame and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ne(360)
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator !=:

>>> df["degrees"] != 360
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values get mapped to False values.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values get mapped to False values.

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Count number of distinct elements in specified axis.

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns.

Get Exponential power of dataframe and other, element-wise (binary operator **).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].pow(df['B'])
0      1
1     32
2    729
dtype: Int64

You can also use arithmetic operator **:

>>> df['A'] ** (df['B'])
0      1
1     32
2    729
dtype: Int64

Return the product of the values over the requested axis.

Return the product of the values over the requested axis.

Get addition of DataFrame and other, element-wise (binary operator +).

Equivalent to dataframe + other. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].add(df['B'])
0    5
1    7
2    9
dtype: Int64

You can also use arithmetic operator +:

>>> df['A'] + (df['B'])
0    5
1    7
2    9
dtype: Int64

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to other / dataframe. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rtruediv(df['B'])
0    4.0
1    2.5
2    2.0
dtype: Float64

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

Conform DataFrame to new index with optional filling logic.

Places NA in locations having no value in the previous index. A new object is produced.

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing Null in locations having no value in the previous index.

Rename columns.

Dict values must be unique (1-to-1). Labels not contained in a dict will be left as-is. Extra labels listed don't throw an error.

Set the name of the axis for the index.

Rearrange index levels using input order. May not drop or duplicate levels.

Reset the index.

Reset the index of the DataFrame, and use the default one instead.

Get integer division of DataFrame and other, element-wise (binary operator //).

Equivalent to other // dataframe. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rfloordiv(df['B'])
0    4
1    2
2    2
dtype: Int64

It's equivalent to using arithmetic operator: //:

>>> df['B'] // (df['A'])
0    4
1    2
2    2
dtype: Int64

Get modulo of DataFrame and other, element-wise (binary operator %).

Equivalent to other % dataframe. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rmod(df['B'])
0    0
1    1
2    0
dtype: Int64

It's equivalent to using arithmetic operator: %:

>>> df['B'] % (df['A'])
0    0
1    1
2    0
dtype: Int64

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to dataframe * other. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Provide rolling window calculations.

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rpow(df['B'])
0      4
1     25
2    216
dtype: Int64

It's equivalent to using arithmetic operator: **:

>>> df['B'] ** (df['A'])
0      4
1     25
2    216
dtype: Int64

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to other - dataframe. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rsub(df['B'])
0    3
1    3
2    3
dtype: Int64

It's equivalent to using arithmetic operator: -:

>>> df['B'] - (df['A'])
0    3
1    3
2    3
dtype: Int64

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to other / dataframe. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rtruediv(df['B'])
0    4.0
1    2.5
2    2.0
dtype: Float64

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one existing column. The index can replace the existing index.

Shift index by desired number of periods.

Shifts the index without realigning the data.

Return unbiased skew over requested axis.

Normalized by N-1.

Sort object by labels (along an axis).

Sort by the values along row axis.

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

• if the columns have a single level, the output is a Series;
• if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Return sample standard deviation over requested axis.

Normalized by N-1 by default.

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to dataframe - other. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].sub(df['B'])
0    -3
1    -3
2    -3
dtype: Int64

You can also use arithmetic operator -:

>>> df['A'] - (df['B'])
0    -3
1    -3
2    -3
dtype: Int64

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to dataframe - other. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].sub(df['B'])
0    -3
1    -3
2    -3
dtype: Int64

You can also use arithmetic operator -:

>>> df['A'] - (df['B'])
0    -3
1    -3
2    -3
dtype: Int64

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.