Class DataFrame (1.29.0)

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.T
      0  1
col1  1  2
col2  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> import bigframes.pandas as bpd
>>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...   index=[4, 5, 6], columns=['A', 'B', 'C'])
>>> bpd.options.display.progress_bar = None
>>> df
    A   B   C
4   0   2   3
5   0   4   1
6  10  20  30
<BLANKLINE>
[3 rows x 3 columns]

>>> df.at[4, 'B']
np.int64(2)

>>> df.loc[5].at['B']
np.int64(4)

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

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> df
      Name  Age  Location
10   Alice   25   Seattle
20     Bob   30  New York
30  Aritra   35      Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.columns
Index(['Name', 'Age', 'Location'], dtype='object')

>>> df.columns = ["NewName", "NewAge", "NewLocation"]
>>> df
   NewName  NewAge NewLocation
10   Alice      25     Seattle
20     Bob      30    New York
30  Aritra      35        Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.columns
Index(['NewName', 'NewAge', 'NewLocation'], dtype='object')

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

>>> df = bpd.DataFrame({'float': [1.0], 'int': [1], 'string': ['foo']})
>>> df.dtypes
float             Float64
int                 Int64
string    string[pyarrow]
dtype: object

>>> import bigframes.pandas as bpd
>>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...                    columns=['A', 'B', 'C'])
>>> bpd.options.display.progress_bar = None
>>> df
    A       B       C
0   0       2       3
1   0       4       1
2   10      20      30
<BLANKLINE>
[3 rows x 3 columns]

>>> df.iat[1, 2]
np.int64(1)

>>> df.loc[0].iat[1]
np.int64(2)

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

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> df
      Name  Age  Location
10   Alice   25   Seattle
20     Bob   30  New York
30  Aritra   35      Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.index # doctest: +ELLIPSIS
Index([10, 20, 30], dtype='Int64')
>>> df.index.values
array([10, 20, 30])

>>> df1 = df.set_index(["Name", "Location"])
>>> df1
                 Age
Name   Location
Alice  Seattle    25
Bob    New York   30
Aritra Kona       35
<BLANKLINE>
[3 rows x 1 columns]
>>> df1.index # doctest: +ELLIPSIS
MultiIndex([( 'Alice',  'Seattle'),
    (   'Bob', 'New York'),
    ('Aritra',     'Kona')],
   names=['Name', 'Location'])
>>> df1.index.values
array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')],
    dtype=object)

>>> 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)

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

>>> s = bpd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3

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

>>> 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)

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

>>> df = bpd.DataFrame({
...         'height': [1.5, 2.6],
...         'weight': [500, 800]
...     },
...     index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800
<BLANKLINE>
[2 rows x 2 columns]

>>> df + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5
<BLANKLINE>
[2 rows x 2 columns]

>>> delta = bpd.DataFrame({
...         'height': [0.5, 0.9],
...         'weight': [50, 80]
...     },
...     index=['elk', 'moose'])
>>> df + delta
       height  weight
elk       2.0     550
moose     3.5     880
<BLANKLINE>
[2 rows x 2 columns]

>>> delta = bpd.DataFrame({
...         'depth': [0.5, 0.9, 1.0],
...         'weight': [50, 80, 100]
...     },
...     index=['elk', 'moose', 'bison'])
>>> df + delta
       depth  height  weight
elk     <NA>    <NA>     550
moose   <NA>    <NA>     880
bison   <NA>    <NA>    <NA>
<BLANKLINE>
[3 rows x 3 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [11, 22, 33]})

>>> np.array(df)
array([[1, 11],
    [2, 22],
    [3, 33]], dtype=object)

>>> np.asarray(df)
array([[1, 11],
    [2, 22],
    [3, 33]], dtype=object)

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

>>> df = bpd.DataFrame({
...         'a': [0, 3, 4],
...         'b': [360, 0, 180]
...      })
>>> df == 0
       a      b
0   True  False
1  False   True
2  False  False
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"a": [15, 15, 15], "b": [30, 30, 30]})
>>> df // 2
   a   b
0  7  15
1  7  15
2  7  15
<BLANKLINE>
[3 rows x 2 columns]

>>> divisor = bpd.DataFrame({"a": [2, 3, 4], "b": [5, 6, 7]})
>>> df // divisor
   a  b
0  7  6
1  5  5
2  3  4
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'a': [0, -1, 1],
...         'b': [1, 0, -1]
...      })
>>> df >= 0
       a      b
0   True   True
1  False   True
2   True  False
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...     "name" : ["alpha", "beta", "gamma"],
...     "age": [20, 30, 40],
...     "location": ["WA", "NY", "CA"]
... })
>>> df
    name  age location
0  alpha   20       WA
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[3 rows x 3 columns]

>>> df["name"]
0    alpha
1     beta
2    gamma
Name: name, dtype: string

>>> df[["name", "age"]]
    name  age
0  alpha   20
1   beta   30
2  gamma   40
<BLANKLINE>
[3 rows x 2 columns]

>>> df[df["age"] > 25]
    name  age location
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[2 rows x 3 columns]

>>> import pandas as pd
>>> df[pd.Index(["age", "location"])]
   age location
0   20       WA
1   30       NY
2   40       CA
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'a': [0, -1, 1],
...         'b': [1, 0, -1]
...      })
>>> df > 0
       a      b
0  False   True
1  False  False
2   True  False
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({'a':[True, False, True], 'b':[-1, 0, 1]})
>>> `df`
       a  b
0  False  0
1   True -1
2  False -2
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'a': [0, -1, 1],
...         'b': [1, 0, -1]
...      })
>>> df <= 0
       a      b
0   True  False
1   True   True
2  False   True
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'a': [0, 1, 2],
...         'b': [3, 4, 5]
...      })
>>> len(df)
3

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

>>> df = bpd.DataFrame({
...         'a': [0, -1, 1],
...         'b': [1, 0, -1]
...      })
>>> df < 0
       a      b
0  False  False
1   True  False
2  False   True
<BLANKLINE>
[3 rows x 2 columns]

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

>>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> left
   0  1   2   3
0  0  1  -2  -1
1  1  1   1   1
<BLANKLINE>
[2 rows x 4 columns]
>>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> right
    0   1
0   0   1
1   1   2
2  -1  -1
3   2   0
<BLANKLINE>
[4 rows x 2 columns]
>>> left @ right
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

>>> right = bpd.Series([1, 2, -1,0])
>>> left @ right
0    4
1    2
dtype: Int64

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df % 3
   a  b
0  1  1
1  2  2
2  0  0
<BLANKLINE>
[3 rows x 2 columns]

>>> modulo = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df % modulo
   a  b
0  1  1
1  0  2
2  1  0
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df * 3
   a   b
0  3  12
1  6  15
2  9  18
<BLANKLINE>
[3 rows x 2 columns]

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df * df1
   a   b
0  2  12
1  4  15
2  6  18
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'a': [0, 3, 4],
...         'b': [360, 0, 180]
...      })
>>> df != 0
       a      b
0  False   True
1   True  False
2   True   True
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df ** 2
   a   b
0  1  16
1  4  25
2  9  36
<BLANKLINE>
[3 rows x 2 columns]

>>> exponent = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df ** exponent
   a    b
0  1   64
1  4  125
2  9  216
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...         'height': [1.5, 2.6],
...         'weight': [500, 800]
...     },
...     index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800
<BLANKLINE>
[2 rows x 2 columns]

>>> df + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5
<BLANKLINE>
[2 rows x 2 columns]

>>> delta = bpd.DataFrame({
...         'height': [0.5, 0.9],
...         'weight': [50, 80]
...     },
...     index=['elk', 'moose'])
>>> df + delta
       height  weight
elk       2.0     550
moose     3.5     880
<BLANKLINE>
[2 rows x 2 columns]

>>> delta = bpd.DataFrame({
...         'depth': [0.5, 0.9, 1.0],
...         'weight': [50, 80, 100]
...     },
...     index=['elk', 'moose', 'bison'])
>>> df + delta
       depth  height  weight
elk     <NA>    <NA>     550
moose   <NA>    <NA>     880
bison   <NA>    <NA>    <NA>
<BLANKLINE>
[3 rows x 3 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df * 3
   a   b
0  3  12
1  6  15
2  9  18
<BLANKLINE>
[3 rows x 2 columns]

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df * df1
   a   b
0  2  12
1  4  15
2  6  18
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({
...     "name" : ["alpha", "beta", "gamma"],
...     "age": [20, 30, 40],
...     "location": ["WA", "NY", "CA"]
... })
>>> df
    name  age location
0  alpha   20       WA
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[3 rows x 3 columns]

>>> df["country"] = "USA"
>>> df
    name  age location country
0  alpha   20       WA     USA
1   beta   30       NY     USA
2  gamma   40       CA     USA
<BLANKLINE>
[3 rows x 4 columns]

>>> df["new_age"] = df["age"] + 5
>>> df
    name  age location country  new_age
0  alpha   20       WA     USA       25
1   beta   30       NY     USA       35
2  gamma   40       CA     USA       45
<BLANKLINE>
[3 rows x 5 columns]

>>> df["new_age"] = bpd.Series([29, 39, 19], index=[1, 2, 0])
>>> df
    name  age location country  new_age
0  alpha   20       WA     USA       19
1   beta   30       NY     USA       29
2  gamma   40       CA     USA       39
<BLANKLINE>
[3 rows x 5 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df - 2
    a  b
0  -1  2
1   0  3
2   1  4
<BLANKLINE>
[3 rows x 2 columns]

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df - df1
    a  b
0  -1  1
1   0  2
2   1  3
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df / 2
     a    b
0  0.5  2.0
1  1.0  2.5
2  1.5  3.0
<BLANKLINE>
[3 rows x 2 columns]

>>> denominator = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df / denominator
    a         b
0  0.5  1.333333
1  1.0  1.666667
2  1.5       2.0
<BLANKLINE>
[3 rows x 2 columns]

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

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

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

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

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.agg('sum')
A    6
B    6
dtype: Int64

>>> df.agg(['sum', 'mean'])
          A   B
sum     6.0 6.0
mean        2.0     2.0
<BLANKLINE>
[2 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.agg('sum')
A    6
B    6
dtype: Int64

>>> df.agg(['sum', 'mean'])
          A   B
sum     6.0 6.0
mean        2.0     2.0
<BLANKLINE>
[2 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]})
>>> df
        A        B
0    True    False
1    True    False
<BLANKLINE>
[2 rows x 2 columns]

>>> df.all()
A     True
B    False
dtype: boolean

>>> df.all(axis=1)
0    False
1    False
dtype: boolean

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

>>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]})
>>> df
        A        B
0    True    False
1    True    False
<BLANKLINE>
[2 rows x 2 columns]

>>> df.any()
A     True
B    False
dtype: boolean

>>> df.any(axis=1)
0    True
1    True
dtype: boolean

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

>>> def square(x):
...     return x * x

>>> df.apply(square)
   col1  col2
0     1     9
1     4    16
<BLANKLINE>
[2 rows x 2 columns]

>>> @bpd.remote_function(reuse=False)
... def foo(row: pd.Series) -> int:
...     result = 1
...     result += row["col1"]
...     result += row["col2"]*row["col2"]
...     return result

>>> df[["col1", "col2"]].apply(foo, axis=1)
0    11
1    19
dtype: Int64

>>> df = bpd.DataFrame({
...     'col1': [1, 2],
...     'col2': [3, 4],
...     'col3': [5, 5]
... })
>>> df
   col1  col2  col3
0     1     3     5
1     2     4     5
<BLANKLINE>
[2 rows x 3 columns]

>>> @bpd.remote_function(reuse=False)
... def foo(x: int, y: int, z: int) -> float:
...     result = 1
...     result += x
...     result += y/z
...     return result

>>> df.apply(foo, axis=1)
0    2.6
1    3.8
dtype: Float64

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

>>> @bpd.remote_function(reuse=False)
... def minutes_to_hours(x: int) -> float:
...     return x/60

>>> df_minutes = bpd.DataFrame(
...     {"system_minutes" : [0, 30, 60, 90, 120],
...      "user_minutes" : [0, 15, 75, 90, 6]})
>>> df_minutes
system_minutes  user_minutes
0               0             0
1              30            15
2              60            75
3              90            90
4             120             6
<BLANKLINE>
[5 rows x 2 columns]

>>> df_hours = df_minutes.map(minutes_to_hours)
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3             1.5           1.5
4             2.0           0.1
<BLANKLINE>
[5 rows x 2 columns]

>>> df_minutes = bpd.DataFrame(
...     {
...         "system_minutes" : [0, 30, 60, None, 90, 120, bpd.NA],
...         "user_minutes" : [0, 15, 75, 90, 6, None, bpd.NA]
...     }, dtype="Int64")
>>> df_hours = df_minutes.map(minutes_to_hours, na_action='ignore')
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3            <NA>           1.5
4             1.5           0.1
5             2.0          <NA>
6            <NA>          <NA>
<BLANKLINE>
[7 rows x 2 columns]

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

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = bpd.DataFrame(data=d)
>>> df.dtypes
col1    Int64
col2    Int64
dtype: object

>>> df.astype('Float64').dtypes
col1    Float64
col2    Float64
dtype: object

>>> ser = bpd.Series([2023010000246789, 1624123244123101, 1054834234120101], dtype='Int64')
>>> ser
0    2023010000246789
1    1624123244123101
2    1054834234120101
dtype: Int64

>>> ser.astype('Float64')
0    2023010000246789.0
1    1624123244123101.0
2    1054834234120101.0
dtype: Float64

>>> ser.astype("timestamp[us, tz=UTC][pyarrow]")
0    2034-02-08 11:13:20.246789+00:00
1    2021-06-19 17:20:44.123101+00:00
2    2003-06-05 17:30:34.120101+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

>>> bpd.to_datetime(ser, unit='us', utc=True)
0    2034-02-08 11:13:20.246789+00:00
1    2021-06-19 17:20:44.123101+00:00
2    2003-06-05 17:30:34.120101+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

>>> timestamp_ser = ser.astype("timestamp[us, tz=UTC][pyarrow]")
>>> timestamp_ser.astype('Int64')
0    2023010000246789
1    1624123244123101
2    1054834234120101
dtype: Int64

>>> 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]

>>> 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]

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

>>> s = bpd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: Int64

>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: Int64

>>> s.loc['b'] = 22
>>> s
a     1
b    22
dtype: Int64
>>> s_copy
a    1
b    2
dtype: Int64

>>> df = bpd.DataFrame({'a': [1, 3], 'b': [2, 4]})
>>> df
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df_copy = df.copy()
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.loc[df["b"] == 2, "b"] = 22
>>> df
   a   b
0  1  22
1  3   4
<BLANKLINE>
[2 rows x 2 columns]
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600],
...                    'C': [0.8, 0.4, 0.9]})
>>> df.corr(numeric_only=True)
          A         B         C
A       1.0       1.0  0.188982
B       1.0       1.0  0.188982
C  0.188982  0.188982       1.0
<BLANKLINE>
[3 rows x 3 columns]

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

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
...                     "B": [1, 2, 3, 4, 5],
...                     "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
       A    B          C
0    1.0    1       <NA>
1   <NA>    2        3.5
2    3.0    3       <NA>
3    4.0    4        4.5
4    5.0    5        5.0
<BLANKLINE>
[5 rows x 3 columns]

>>> df.count()
A    4
B    5
C    3
dtype: Int64

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600],
...                    'C': [0.8, 0.4, 0.9]})
>>> df.cov(numeric_only=True)
       A        B     C
A    1.0    100.0  0.05
B  100.0  10000.0   5.0
C   0.05      5.0  0.07
<BLANKLINE>
[3 rows x 3 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cummax()
    A       B
0   3       1
1   3       2
2   3       3
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cummin()
    A       B
0   3       1
1   1       1
2   1       1
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cumprod()
     A    B
0  3.0  1.0
1  3.0  2.0
2  6.0  6.0
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cumsum()
    A       B
0   3       1
1   4       3
2   6       6
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [0, 2, 8]})
>>> df
    A       B
0   3       0
1   1       2
2   2       8
<BLANKLINE>
[3 rows x 2 columns]

>>> df.describe()
              A               B
count       3.0             3.0
mean        2.0        3.333333
std         1.0        4.163332
min         1.0             0.0
25%         1.0             0.0
50%         2.0             2.0
75%         3.0             8.0
max         3.0             8.0
<BLANKLINE>
[8 rows x 2 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.diff()
       A       B
0   <NA>    <NA>
1     -2       1
2      1       1
<BLANKLINE>
[3 rows x 2 columns]

>>> df.diff(periods=-1)
       A       B
0      2      -1
1     -1      -1
2   <NA>    <NA>
<BLANKLINE>
[3 rows x 2 columns]

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

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

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

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

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

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

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

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

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

>>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> left
   0  1   2   3
0  0  1  -2  -1
1  1  1   1   1
<BLANKLINE>
[2 rows x 4 columns]
>>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> right
    0   1
0   0   1
1   1   2
2  -1  -1
3   2   0
<BLANKLINE>
[4 rows x 2 columns]
>>> left.dot(right)
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

>>> left @ right
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

>>> right = bpd.Series([1, 2, -1,0])
>>> left @ right
0    4
1    2
dtype: Int64

>>> left = bpd.DataFrame([[1, 2, 3], [2, 5, 7]], index=["alpha", "beta"])
>>> left
       0  1  2
alpha  1  2  3
beta   2  5  7
<BLANKLINE>
[2 rows x 3 columns]
>>> right = bpd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]], columns=["red", "green", "blue"])
>>> right
   red  green  blue
0    2      4     8
1    1      5    10
2    3      6     9
<BLANKLINE>
[3 rows x 3 columns]
>>> left.dot(right)
       red  green  blue
alpha   13     32    55
beta    30     75   129
<BLANKLINE>
[2 rows x 3 columns]

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

>>> df = bpd.DataFrame(np.arange(12).reshape(3, 4),
...                    columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
<BLANKLINE>
[3 rows x 4 columns]

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
<BLANKLINE>
[3 rows x 2 columns]

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11
<BLANKLINE>
[3 rows x 2 columns]

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11
<BLANKLINE>
[1 rows x 4 columns]

>>> import pandas as pd
>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = bpd.DataFrame(index=midx, columns=['big', 'small'],
...                    data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                          [250, 150], [1.5, 0.8], [320, 250],
...                          [1, 0.8], [0.3, 0.2]])
>>> df
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
       length    1.5    1.0
cow    speed    30.0   20.0
       weight  250.0  150.0
       length    1.5    0.8
falcon speed   320.0  250.0
       weight    1.0    0.8
       length    0.3    0.2
<BLANKLINE>
[9 rows x 2 columns]

>>> df.drop(index='cow', columns='small')
                 big
llama  speed    45.0
       weight  200.0
       length    1.5
falcon speed   320.0
       weight    1.0
       length    0.3
<BLANKLINE>
[6 rows x 1 columns]

>>> df.drop(index='length', level=1)
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
cow    speed    30.0   20.0
       weight  250.0  150.0
falcon speed   320.0  250.0
       weight    1.0    0.8
<BLANKLINE>
[6 rows x 2 columns]

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

>>> df = bpd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                     "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                     "born": [bpd.NA, "1940-04-25", bpd.NA]})
>>> df
       name        toy        born
0    Alfred       <NA>        <NA>
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[3 rows x 3 columns]

>>> df.dropna()
     name        toy        born
1  Batman  Batmobile  1940-04-25
<BLANKLINE>
[1 rows x 3 columns]

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman
<BLANKLINE>
[3 rows x 1 columns]

>>> df.dropna(how='all')
       name        toy        born
0    Alfred       <NA>        <NA>
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[3 rows x 3 columns]

>>> df.dropna(subset=['name', 'toy'])
       name        toy        born
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[2 rows x 3 columns]

>>> 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"].eq(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

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

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

>>> df = bpd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
<BLANKLINE>
[5 rows x 2 columns]
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: Int64

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
<BLANKLINE>
[5 rows x 3 columns]
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
<BLANKLINE>
[5 rows x 2 columns]

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
<BLANKLINE>
[5 rows x 4 columns]

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

>>> df = bpd.DataFrame({'A': [[0, 1, 2], [], [], [3, 4]],
...                     'B': 1,
...                     'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df.explode('A')
    A  B              C
0     0  1  ['a' 'b' 'c']
0     1  1  ['a' 'b' 'c']
0     2  1  ['a' 'b' 'c']
1  <NA>  1             []
2  <NA>  1             []
3     3  1      ['d' 'e']
3     4  1      ['d' 'e']
<BLANKLINE>
[7 rows x 3 columns]
>>> df.explode(list('AC'))
    A  B     C
0     0  1     a
0     1  1     b
0     2  1     c
1  <NA>  1  <NA>
2  <NA>  1  <NA>
3     3  1     d
3     4  1     e
<BLANKLINE>
[7 rows x 3 columns]

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

>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
...                     [3, 4, np.nan, 1],
...                     [np.nan, np.nan, np.nan, np.nan],
...                     [np.nan, 3, np.nan, 4]],
...                    columns=list("ABCD")).astype("Float64")
>>> df
      A     B     C     D
0  <NA>   2.0  <NA>   0.0
1   3.0   4.0  <NA>   1.0
2  <NA>  <NA>  <NA>  <NA>
3  <NA>   3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

>>> df.ffill()
      A    B     C    D
0  <NA>  2.0  <NA>  0.0
1   3.0  4.0  <NA>  1.0
2   3.0  4.0  <NA>  1.0
3   3.0  3.0  <NA>  4.0
<BLANKLINE>
[4 rows x 4 columns]

>>> series = bpd.Series([1, np.nan, 2, 3])
>>> series.ffill()
0    1.0
1    1.0
2    2.0
3    3.0
dtype: Float64

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

>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
...                     [3, 4, np.nan, 1],
...                     [np.nan, np.nan, np.nan, np.nan],
...                     [np.nan, 3, np.nan, 4]],
...                    columns=list("ABCD")).astype("Float64")
>>> df
    A     B     C     D
0  <NA>   2.0  <NA>   0.0
1   3.0   4.0  <NA>   1.0
2  <NA>  <NA>  <NA>  <NA>
3  <NA>   3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

>>> df.fillna(0)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0
<BLANKLINE>
[4 rows x 4 columns]

>>> df_fill = bpd.DataFrame(np.arange(12).reshape(3, 4),
...                         columns=['A', 'B', 'C', 'D'])
>>> df_fill
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
<BLANKLINE>
[3 rows x 4 columns]
>>> df.fillna(df_fill)
    A    B     C     D
0   0.0  2.0   2.0   0.0
1   3.0  4.0   6.0   1.0
2   8.0  9.0  10.0  11.0
3  <NA>  3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

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

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

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

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

>>> 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"].ge(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

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

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

>>> df = bpd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                                'Parrot', 'Parrot'],
...                     'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
<BLANKLINE>
[4 rows x 2 columns]

>>> df.groupby(['Animal'])['Max Speed'].mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

>>> df = bpd.DataFrame([[1, 2, 3],[1, None, 4], [2, 1, 3], [1, 2, 2]],
...                    columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
     a  c
b
1.0  2  3
2.0  2  5
<BLANKLINE>
[2 rows x 2 columns]

>>> df.groupby(by=["b"], dropna=False).sum()
      a  c
b
1.0   2  3
2.0   2  5
<NA>  1  4
<BLANKLINE>
[3 rows x 2 columns]

>>> df.groupby(by=["b"], as_index=False).sum()
     b  a  c
0  1.0  2  3
1  2.0  2  5
<BLANKLINE>
[2 rows x 3 columns]

>>> 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

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

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

>>> df = bpd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                     'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra
<BLANKLINE>
[9 rows x 1 columns]

>>> df.head()
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
<BLANKLINE>
[5 rows x 1 columns]

>>> df.head(3)
    animal
0  alligator
1        bee
2     falcon
<BLANKLINE>
[3 rows x 1 columns]

>>> df.head(-3)
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
<BLANKLINE>
[6 rows x 1 columns]

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.idxmax()
A    0
B    2
dtype: Int64

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.idxmin()
A    1
B    0
dtype: Int64

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

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

>>> df.insert(1, 'col3', 5)
>>> df
   col1  col3  col2
0     1     5     3
1     2     5     4
<BLANKLINE>
[2 rows x 3 columns]

>>> df.insert(0, 'col2', [5, 6], allow_duplicates=True)
>>> df
   col2  col1  col3  col2
0     5     1     5     3
1     6     2     5     4
<BLANKLINE>
[2 rows x 4 columns]

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3, None, None, 6],
...     'B': [None, 6, None, 2, None, 3],
...     }, index=[0, 0.1, 0.3, 0.7, 0.9, 1.0])
>>> df.interpolate()
       A     B
0.0  1.0  <NA>
0.1  2.0   6.0
0.3  3.0   4.0
0.7  4.0   2.0
0.9  5.0   2.5
1.0  6.0   3.0
<BLANKLINE>
[6 rows x 2 columns]
>>> df.interpolate(method="values")
            A         B
0.0       1.0      <NA>
0.1       2.0       6.0
0.3       3.0  4.666667
0.7  4.714286       2.0
0.9  5.571429  2.666667
1.0       6.0       3.0
<BLANKLINE>
[6 rows x 2 columns]

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

>>> df = bpd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
...                    index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
<BLANKLINE>
[2 rows x 2 columns]

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True
<BLANKLINE>
[2 rows x 2 columns]

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True
<BLANKLINE>
[2 rows x 2 columns]

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0       5
1    <NA>
2       6
3    <NA>
4    <NA>
dtype: Int64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0       5
1    <NA>
2       6
3    <NA>
4    <NA>
dtype: Int64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

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

>>> df = bpd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                     'population': [1864, 22000, 80000]},
...                    index=['panda', 'polar', 'koala'])
>>> df
         species  population
panda       bear        1864
polar       bear       22000
koala  marsupial       80000
<BLANKLINE>
[3 rows x 2 columns]

>>> for label, content in df.items():
...     print(f'--> label: {label}')
...     print(f'--> content:\n{content}')
...
--> label: species
--> content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: string
--> label: population
--> content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: Int64

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> index, row = next(df.iterrows())
>>> index
np.int64(0)
>>> row
A    1
B    4
Name: 0, dtype: object

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> next(df.itertuples(name="Pair"))
Pair(Index=np.int64(0), A=np.int64(1), B=np.int64(4))

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

>>> df1 = bpd.DataFrame({'col1': ['foo', 'bar'], 'col2': [1, 2]}, index=[10, 11])
>>> df1
   col1  col2
10  foo     1
11  bar     2
<BLANKLINE>
[2 rows x 2 columns]

>>> df2 = bpd.DataFrame({'col3': ['foo', 'baz'], 'col4': [3, 4]}, index=[11, 22])
>>> df2
   col3  col4
11  foo     3
22  baz     4
<BLANKLINE>
[2 rows x 2 columns]

>>> df1.join(df2)
   col1  col2  col3  col4
10  foo     1  <NA>  <NA>
11  bar     2   foo     3
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="left")
   col1  col2  col3  col4
10  foo     1  <NA>  <NA>
11  bar     2   foo     3
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="right")
    col1  col2 col3  col4
11  bar      2  foo     3
22  <NA>  <NA>  baz     4
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="outer")
    col1  col2  col3  col4
10   foo     1  <NA>  <NA>
11   bar     2   foo     3
22  <NA>  <NA>   baz     4
<BLANKLINE>
[3 rows x 4 columns]

>>> df1.join(df2, how="inner")
   col1  col2 col3  col4
11  bar     2  foo     3
<BLANKLINE>
[1 rows x 4 columns]

>>> df1.join(df2, on="col1", how="right")
      col1  col2 col3  col4
<NA>    11  <NA>  foo     3
<NA>    22  <NA>  baz     4
<BLANKLINE>
[2 rows x 4 columns]

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

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

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

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
...                     "B": [3, 4, 3, 2, 1],
...                     "C": [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       3       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2
<BLANKLINE>
[5 rows x 3 columns]

>>> df.kurt()
A        -1.2
B   -0.177515
C         5.0
dtype: Float64