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#  pandas.DataFrame.bool  #

DataFrame.  bool  (  )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/generic.py#L1584-L1649) #

    

Return the bool of a single element Series or DataFrame.

Deprecated since version 2.1.0:  bool is deprecated and will be removed in
future version of pandas. For ` Series  ` use ` pandas.Series.item  ` .

This must be a boolean scalar value, either True or False. It will raise a
ValueError if the Series or DataFrame does not have exactly 1 element, or that
element is not boolean (integer values 0 and 1 will also raise an exception).

Returns  :

    

bool

    

The value in the Series or DataFrame.

See also

[ ` Series.astype  ` ](pandas.Series.astype.html#pandas.Series.astype
"pandas.Series.astype")

    

Change the data type of a Series, including to boolean.

[ ` DataFrame.astype  ` ](pandas.DataFrame.astype.html#pandas.DataFrame.astype
"pandas.DataFrame.astype")

    

Change the data type of a DataFrame, including to boolean.

[ ` numpy.bool_  `
](https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_ "\(in
NumPy v1.26\)")

    

NumPy boolean data type, used by pandas for boolean values.

Examples

The method will only work for single element objects with a boolean value:

    
    
    >>> pd.Series([True]).bool()  
    True
    >>> pd.Series([False]).bool()  
    False
    
    
    
    >>> pd.DataFrame({'col': [True]}).bool()  
    True
    >>> pd.DataFrame({'col': [False]}).bool()  
    False
    

This is an alternative method and will only work for single element objects
with a boolean value:

    
    
    >>> pd.Series([True]).item()  
    True
    >>> pd.Series([False]).item()  
    False
    

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#  pandas.DataFrame.head  #

DataFrame.  head  (  _ n  =  5  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/generic.py#L5818-L5893) #

    

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.

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.

Parameters  :

    

**n** int, default 5

    

Number of rows to select.

Returns  :

    

same type as caller

    

The first  n  rows of the caller object.

See also

[ ` DataFrame.tail  ` ](pandas.DataFrame.tail.html#pandas.DataFrame.tail
"pandas.DataFrame.tail")

    

Returns the last  n  rows.

Examples

    
    
    >>> df = pd.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
    

Viewing the first 5 lines

    
    
    >>> df.head()
          animal
    0  alligator
    1        bee
    2     falcon
    3       lion
    4     monkey
    

Viewing the first  n  lines (three in this case)

    
    
    >>> df.head(3)
          animal
    0  alligator
    1        bee
    2     falcon
    

For negative values of  n

    
    
    >>> df.head(-3)
          animal
    0  alligator
    1        bee
    2     falcon
    3       lion
    4     monkey
    5     parrot
    

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#  pandas.DataFrame.count  #

DataFrame.  count  (  _ axis  =  0  _ , _ numeric_only  =  False  _ )  [
[source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L11358-L11433) #

    

Count non-NA cells for each column or row.

The values  None  ,  NaN  ,  NaT  , ` pandas.NA  ` are considered NA.

Parameters  :

    

**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0

    

If 0 or âindexâ counts are generated for each column. If 1 or
âcolumnsâ counts are generated for each row.

**numeric_only** bool, default False

    

Include only  float  ,  int  or  boolean  data.

Returns  :

    

Series

    

For each column/row the number of non-NA/null entries.

See also

[ ` Series.count  ` ](pandas.Series.count.html#pandas.Series.count
"pandas.Series.count")

    

Number of non-NA elements in a Series.

[ ` DataFrame.value_counts  `
](pandas.DataFrame.value_counts.html#pandas.DataFrame.value_counts
"pandas.DataFrame.value_counts")

    

Count unique combinations of columns.

[ ` DataFrame.shape  ` ](pandas.DataFrame.shape.html#pandas.DataFrame.shape
"pandas.DataFrame.shape")

    

Number of DataFrame rows and columns (including NA elements).

[ ` DataFrame.isna  ` ](pandas.DataFrame.isna.html#pandas.DataFrame.isna
"pandas.DataFrame.isna")

    

Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

    
    
    >>> df = pd.DataFrame({"Person":
    ...                    ["John", "Myla", "Lewis", "John", "Myla"],
    ...                    "Age": [24., np.nan, 21., 33, 26],
    ...                    "Single": [False, True, True, True, False]})
    >>> df
       Person   Age  Single
    0    John  24.0   False
    1    Myla   NaN    True
    2   Lewis  21.0    True
    3    John  33.0    True
    4    Myla  26.0   False
    

Notice the uncounted NA values:

    
    
    >>> df.count()
    Person    5
    Age       4
    Single    5
    dtype: int64
    

Counts for each **row** :

    
    
    >>> df.count(axis='columns')
    0    3
    1    2
    2    3
    3    3
    4    3
    dtype: int64
    

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#  pandas.DataFrame.isna  #

DataFrame.  isna  (  )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L6488-L6492) #

    

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values,
such as None or ` numpy.NaN  ` , gets mapped to True values. Everything else
gets mapped to False values. Characters such as empty strings ` ''  ` or `
numpy.inf  ` are not considered NA values (unless you set `
pandas.options.mode.use_inf_as_na  =  True  ` ).

Returns  :

    

DataFrame

    

Mask of bool values for each element in DataFrame that indicates whether an
element is an NA value.

See also

[ ` DataFrame.isnull  ` ](pandas.DataFrame.isnull.html#pandas.DataFrame.isnull
"pandas.DataFrame.isnull")

    

Alias of isna.

[ ` DataFrame.notna  ` ](pandas.DataFrame.notna.html#pandas.DataFrame.notna
"pandas.DataFrame.notna")

    

Boolean inverse of isna.

[ ` DataFrame.dropna  ` ](pandas.DataFrame.dropna.html#pandas.DataFrame.dropna
"pandas.DataFrame.dropna")

    

Omit axes labels with missing values.

[ ` isna  ` ](pandas.isna.html#pandas.isna "pandas.isna")

    

Top-level isna.

Examples

Show which entries in a DataFrame are NA.

    
    
    >>> df = pd.DataFrame(dict(age=[5, 6, np.nan],
    ...                        born=[pd.NaT, pd.Timestamp('1939-05-27'),
    ...                              pd.Timestamp('1940-04-25')],
    ...                        name=['Alfred', 'Batman', ''],
    ...                        toy=[None, 'Batmobile', 'Joker']))
    >>> df
       age       born    name        toy
    0  5.0        NaT  Alfred       None
    1  6.0 1939-05-27  Batman  Batmobile
    2  NaN 1940-04-25              Joker
    
    
    
    >>> df.isna()
         age   born   name    toy
    0  False   True  False   True
    1  False  False  False  False
    2   True  False  False  False
    

Show which entries in a Series are NA.

    
    
    >>> ser = pd.Series([5, 6, np.nan])
    >>> ser
    0    5.0
    1    6.0
    2    NaN
    dtype: float64
    
    
    
    >>> ser.isna()
    0    False
    1    False
    2     True
    dtype: bool
    

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#  pandas.PeriodIndex.dayofyear  #

_ property  _ PeriodIndex.  dayofyear  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/extension.py#L67-L75) #

    

The ordinal day of the year.

Examples

    
    
    >>> idx = pd.PeriodIndex(["2023-01-10", "2023-02-01", "2023-03-01"], freq="D")
    >>> idx.dayofyear
    Index([10, 32, 60], dtype='int64')
    
    
    
    >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y")
    >>> idx
    PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]')
    >>> idx.dayofyear
    Index([365, 366, 365], dtype='int64')
    

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#  pandas.DataFrame.corrwith  #

DataFrame.  corrwith  (  _ other  _ , _ axis  =  0  _ , _ drop  =  False  _ ,
_ method  =  'pearson'  _ , _ numeric_only  =  False  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L11219-L11353) #

    

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with
rows or columns of Series or DataFrame. DataFrames are first aligned along
both axes before computing the correlations.

Parameters  :

    

**other** DataFrame, Series

    

Object with which to compute correlations.

**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0

    

The axis to use. 0 or âindexâ to compute row-wise, 1 or âcolumnsâ for
column-wise.

**drop** bool, default False

    

Drop missing indices from result.

**method** {âpearsonâ, âkendallâ, âspearmanâ} or callable

    

Method of correlation:

  * pearson : standard correlation coefficient 

  * kendall : Kendall Tau correlation coefficient 

  * spearman : Spearman rank correlation 

  * callable: callable with input two 1d ndarrays 
    

and returning a float.

**numeric_only** bool, default False

    

Include only  float  ,  int  or  boolean  data.

New in version 1.5.0.

Changed in version 2.0.0:  The default value of ` numeric_only  ` is now `
False  ` .

Returns  :

    

Series

    

Pairwise correlations.

See also

[ ` DataFrame.corr  ` ](pandas.DataFrame.corr.html#pandas.DataFrame.corr
"pandas.DataFrame.corr")

    

Compute pairwise correlation of columns.

Examples

    
    
    >>> index = ["a", "b", "c", "d", "e"]
    >>> columns = ["one", "two", "three", "four"]
    >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
    >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
    >>> df1.corrwith(df2)
    one      1.0
    two      1.0
    three    1.0
    four     1.0
    dtype: float64
    
    
    
    >>> df2.corrwith(df1, axis=1)
    a    1.0
    b    1.0
    c    1.0
    d    1.0
    e    NaN
    dtype: float64
    

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#  pandas.DataFrame.set_axis  #

DataFrame.  set_axis  (  _ labels  _ , _ *  _ , _ axis  =  0  _ , _ copy  =
None  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/frame.py#L5319-L5357) #

    

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or
Index.

Parameters  :

    

**labels** list-like, Index

    

The values for the new index.

**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0

    

The axis to update. The value 0 identifies the rows. For  Series  this
parameter is unused and defaults to 0.

**copy** bool, default True

    

Whether to make a copy of the underlying data.

Note

The  copy  keyword will change behavior in pandas 3.0. [ Copy-on-Write
](https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html) will be
enabled by default, which means that all methods with a  copy  keyword will
use a lazy copy mechanism to defer the copy and ignore the  copy  keyword. The
copy  keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy
on write ` pd.options.mode.copy_on_write  =  True  `

Returns  :

    

DataFrame

    

An object of type DataFrame.

See also

[ ` DataFrame.rename_axis  `
](pandas.DataFrame.rename_axis.html#pandas.DataFrame.rename_axis
"pandas.DataFrame.rename_axis")

    

Alter the name of the index or columns.

Examples

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

Change the row labels.

    
    
    >>> df.set_axis(['a', 'b', 'c'], axis='index')
       A  B
    a  1  4
    b  2  5
    c  3  6
    

Change the column labels.

    
    
    >>> df.set_axis(['I', 'II'], axis='columns')
       I  II
    0  1   4
    1  2   5
    2  3   6
    

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  * pandas.Multi... 

#  pandas.MultiIndex.levels  #

MultiIndex.  levels  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/multi.py#L844-L901) #

    

Levels of the MultiIndex.

Levels refer to the different hierarchical levels or layers in a MultiIndex.
In a MultiIndex, each level represents a distinct dimension or category of the
index.

To access the levels, you can use the levels attribute of the MultiIndex,
which returns a tuple of Index objects. Each Index object represents a level
in the MultiIndex and contains the unique values found in that specific level.

If a MultiIndex is created with levels A, B, C, and the DataFrame using it
filters out all rows of the level C, MultiIndex.levels will still return A, B,
C.

Examples

    
    
    >>> index = pd.MultiIndex.from_product([['mammal'],
    ...                                     ('goat', 'human', 'cat', 'dog')],
    ...                                    names=['Category', 'Animals'])
    >>> leg_num = pd.DataFrame(data=(4, 2, 4, 4), index=index, columns=['Legs'])
    >>> leg_num
                      Legs
    Category Animals
    mammal   goat        4
             human       2
             cat         4
             dog         4
    
    
    
    >>> leg_num.index.levels
    FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
    

MultiIndex levels will not change even if the DataFrame using the MultiIndex
does not contain all them anymore. See how âhumanâ is not in the
DataFrame, but it is still in levels:

    
    
    >>> large_leg_num = leg_num[leg_num.Legs > 2]
    >>> large_leg_num
                      Legs
    Category Animals
    mammal   goat        4
             cat         4
             dog         4
    
    
    
    >>> large_leg_num.index.levels
    FrozenList([['mammal'], ['cat', 'dog', 'goat', 'human']])
    

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  * ` MultiIndex.levels  `

[ __ Show Source
](../../_sources/reference/api/pandas.MultiIndex.levels.rst.txt)

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