# Essential Basic Functionality

Here we discuss a lot of the essential functionality common to the pandas data structures. Here’s how to create some of the objects used in the examples from the previous section:

    In [1]: index = pd.date_range('1/1/2000', periods=8)

    In [2]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index,
       ...:                   columns=['A', 'B', 'C'])
       ...:

    In [4]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
       ...:               major_axis=pd.date_range('1/1/2000', periods=5),
       ...:               minor_axis=['A', 'B', 'C', 'D'])
       ...:

## Head and Tail

To view a small sample of a Series or DataFrame object, use the `head()` and `tail()` methods. The default number of elements to display is five, but you may pass a custom number.

    In [5]: long_series = pd.Series(np.random.randn(1000))

    In [6]: long_series.head()
    Out[6]:
    0   -0.305384
    1   -0.479195
    2    0.095031
    3   -0.270099
    4   -0.707140
    dtype: float64

    In [7]: long_series.tail(3)
    Out[7]:
    997    0.588446
    998    0.026465
    999   -1.728222
    dtype: float64

## Attributes and the raw ndarray(s)

pandas objects have a number of attributes enabling you to access the metadata

> - shape: gives the axis dimensions of the object, consistent with ndarray
> - Axis labels
>   - Series: index (only axis)
>   - DataFrame: index (rows) and columns
>   - Panel: items, major_axis, and minor_axis

Note, these attributes can be safely assigned to!

    In [8]: df[:2]
    Out[8]:
                       A         B         C
    2000-01-01  0.187483 -1.933946  0.377312
    2000-01-02  0.734122  2.141616 -0.011225

    In [9]: df.columns = [x.lower() for x in df.columns]

    In [10]: df
    Out[10]:
                       a         b         c
    2000-01-01  0.187483 -1.933946  0.377312
    2000-01-02  0.734122  2.141616 -0.011225
    2000-01-03  0.048869 -1.360687 -0.479010
    2000-01-04 -0.859661 -0.231595 -0.527750
    2000-01-05 -1.296337  0.150680  0.123836
    2000-01-06  0.571764  1.555563 -0.823761
    2000-01-07  0.535420 -1.032853  1.469725
    2000-01-08  1.304124  1.449735  0.203109

To get the actual data inside a data structure, one need only access the values property:

    In [11]: s.values
    Out[11]: array([ 0.1122,  0.8717, -0.8161, -0.7849,  1.0307])

    In [12]: df.values
    Out[12]:
    array([[ 0.1875, -1.9339,  0.3773],
           [ 0.7341,  2.1416, -0.0112],
           [ 0.0489, -1.3607, -0.479 ],
           [-0.8597, -0.2316, -0.5278],
           [-1.2963,  0.1507,  0.1238],
           [ 0.5718,  1.5556, -0.8238],
           [ 0.5354, -1.0329,  1.4697],
           [ 1.3041,  1.4497,  0.2031]])

    In [13]: wp.values
    Out[13]:
    array([[[-1.032 ,  0.9698, -0.9627,  1.3821],
            [-0.9388,  0.6691, -0.4336, -0.2736],
            [ 0.6804, -0.3084, -0.2761, -1.8212],
            [-1.9936, -1.9274, -2.0279,  1.625 ],
            [ 0.5511,  3.0593,  0.4553, -0.0307]],

           [[ 0.9357,  1.0612, -2.1079,  0.1999],
            [ 0.3236, -0.6416, -0.5875,  0.0539],
            [ 0.1949, -0.382 ,  0.3186,  2.0891],
            [-0.7283, -0.0903, -0.7482,  1.3189],
            [-2.0298,  0.7927,  0.461 , -0.5427]]])

If a DataFrame or Panel contains homogeneously-typed data, the ndarray can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case. The values attribute itself, unlike the axis labels, cannot be assigned to.

Note

When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype. If there are only floats and integers, the resulting array will be of float dtype.

## Accelerated operations

pandas has support for accelerating certain types of binary numerical and boolean operations using the `numexpr` library (starting in 0.11.0) and the `bottleneck` libraries.

These libraries are especially useful when dealing with large data sets, and provide large speedups. `numexpr` uses smart chunking, caching, and multiple cores. `bottleneck` is a set of specialized cython routines that are especially fast when dealing with arrays that have `nans`.

Here is a sample (using 100 column x 100,000 row `DataFrames`):

Operation | 0.11.0 (ms) | Prior Version (ms) | Ratio to Prior
--- | --- | --- | ---
`df1 > df2` | 13.32 | 125.35 | 0.1063
`df1 * df2` | 21.71 | 36.63 | 0.5928
`df1 + df2` | 22.04 | 36.50 | 0.6039

You are highly encouraged to install both libraries. See the section Recommended Dependencies for more installation info.

## Flexible binary operations

With binary operations between pandas data structures, there are two key points of interest:

> - Broadcasting behavior between higher- (e.g. DataFrame) and lower-dimensional (e.g. Series) objects.
> - Missing data in computations

We will demonstrate how to manage these issues independently, though they can be handled simultaneously.

### Matching / broadcasting behavior

DataFrame has the methods `add()`, `sub()`, `mul()`, `div()` and related functions `radd()`, `rsub()`, ... for carrying out binary operations. For broadcasting behavior, Series input is of primary interest. Using these functions, you can use to either match on the index or columns via the axis keyword:

    In [14]: df = pd.DataFrame({'one' : pd.Series(np.random.randn(3), index=['a', 'b', 'c']),
       ....:                    'two' : pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),
       ....:                    'three' : pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})
       ....:

    In [15]: df
    Out[15]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [16]: row = df.ix[1]

    In [17]: column = df['two']

    In [18]: df.sub(row, axis='columns')
    Out[18]:
            one     three       two
    a -0.487650       NaN -1.487837
    b  0.000000  0.000000  0.000000
    c  0.150512  0.639504 -1.585038
    d       NaN  1.301762 -2.237808

    In [19]: df.sub(row, axis=1)
    Out[19]:
            one     three       two
    a -0.487650       NaN -1.487837
    b  0.000000  0.000000  0.000000
    c  0.150512  0.639504 -1.585038
    d       NaN  1.301762 -2.237808

    In [20]: df.sub(column, axis='index')
    Out[20]:
            one     three  two
    a -0.274957       NaN    0
    b -1.275144 -1.313539    0
    c  0.460406  0.911003    0
    d       NaN  2.226031    0

    In [21]: df.sub(column, axis=0)
    Out[21]:
            one     three  two
    a -0.274957       NaN    0
    b -1.275144 -1.313539    0
    c  0.460406  0.911003    0
    d       NaN  2.226031    0

Furthermore you can align a level of a multi-indexed DataFrame with a Series.

    In [22]: dfmi = df.copy()

    In [23]: dfmi.index = pd.MultiIndex.from_tuples([(1,'a'),(1,'b'),(1,'c'),(2,'a')],
       ....:                                        names=['first','second'])
       ....:

    In [24]: dfmi.sub(column, axis=0, level='second')
    Out[24]:
                       one     three       two
    first second
    1     a      -0.274957       NaN  0.000000
          b      -1.275144 -1.313539  0.000000
          c       0.460406  0.911003  0.000000
    2     a            NaN  1.476060 -0.749971

With Panel, describing the matching behavior is a bit more difficult, so the arithmetic methods instead (and perhaps confusingly?) give you the option to specify the broadcast axis. For example, suppose we wished to demean the data over a particular axis. This can be accomplished by taking the mean over an axis and broadcasting over the same axis:

    In [25]: major_mean = wp.mean(axis='major')

    In [26]: major_mean
    Out[26]:
          Item1     Item2
    A -0.546569 -0.260774
    B  0.492478  0.147993
    C -0.649010 -0.532794
    D  0.176307  0.623812

    In [27]: wp.sub(major_mean, axis='major')
    Out[27]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: Item1 to Item2
    Major_axis axis: 2000-01-01 00:00:00 to 2000-01-05 00:00:00
    Minor_axis axis: A to D

And similarly for `axis="items"` and `axis="minor"`.

Note

I could be convinced to make the axis argument in the DataFrame methods match the broadcasting behavior of Panel. Though it would require a transition period so users can change their code...

### Missing data / operations with fill values

In Series and DataFrame (though not yet in Panel), the arithmetic functions have the option of inputting a fill_value, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using `fillna` if you wish).

    In [28]: df
    Out[28]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [29]: df2
    Out[29]:
            one     three       two
    a -0.626544  1.000000 -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [30]: df + df2
    Out[30]:
            one     three       two
    a -1.253088       NaN -0.703174
    b -0.277789 -0.354579  2.272499
    c  0.023235  0.924429 -0.897577
    d       NaN  2.248945 -2.203116

    In [31]: df.add(df2, fill_value=0)
    Out[31]:
            one     three       two
    a -1.253088  1.000000 -0.703174
    b -0.277789 -0.354579  2.272499
    c  0.023235  0.924429 -0.897577
    d       NaN  2.248945 -2.203116

### Flexible Comparisons

Starting in v0.8, pandas introduced binary comparison methods eq, ne, lt, gt, le, and ge to Series and DataFrame whose behavior is analogous to the binary arithmetic operations described above:

    In [32]: df.gt(df2)
    Out[32]:
         one  three    two
    a  False  False  False
    b  False  False  False
    c  False  False  False
    d  False  False  False

    In [33]: df2.ne(df)
    Out[33]:
         one  three    two
    a  False   True  False
    b  False  False  False
    c  False  False  False
    d   True  False  False

These operations produce a pandas object the same type as the left-hand-side input that if of dtype `bool`. These `boolean` objects can be used in indexing operations, see here

### Boolean Reductions

You can apply the reductions: `empty`, `any()`, `all()`, and `bool()` to provide a way to summarize a boolean result.

    In [34]: (df > 0).all()
    Out[34]:
    one      False
    three    False
    two      False
    dtype: bool

    In [35]: (df > 0).any()
    Out[35]:
    one      True
    three    True
    two      True
    dtype: bool

You can reduce to a final boolean value.

    In [36]: (df > 0).any().any()
    Out[36]: True

You can test if a pandas object is empty, via the `empty` property.

    In [37]: df.empty
    Out[37]: False

    In [38]: pd.DataFrame(columns=list('ABC')).empty
    Out[38]: True

To evaluate single-element pandas objects in a boolean context, use the method `bool()`:

    In [39]: pd.Series([True]).bool()
    Out[39]: True

    In [40]: pd.Series([False]).bool()
    Out[40]: False

    In [41]: pd.DataFrame([[True]]).bool()
    Out[41]: True

    In [42]: pd.DataFrame([[False]]).bool()
    Out[42]: False

Warning

You might be tempted to do the following:

    >>>if df:
         ...

Or

    >>> df and df2

These both will raise as you are trying to compare multiple values.

    ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

See gotchas for a more detailed discussion.

### Comparing if objects are equivalent

Often you may find there is more than one way to compute the same result. As a simple example, consider `df+df` and `df*2`. To test that these two computations produce the same result, given the tools shown above, you might imagine using `(df+df == df*2).all()`. But in fact, this expression is False:

    In [43]: df+df == df*2
    Out[43]:
         one  three   two
    a   True  False  True
    b   True   True  True
    c   True   True  True
    d  False   True  True

    In [44]: (df+df == df*2).all()
    Out[44]:
    one      False
    three    False
    two       True
    dtype: bool

Notice that the boolean DataFrame `df+df == df*2` contains some False values! That is because NaNs do not compare as equals:

    In [45]: np.nan == np.nan
    Out[45]: False

So, as of v0.13.1, NDFrames (such as Series, DataFrames, and Panels) have an `equals()` method for testing equality, with NaNs in corresponding locations treated as equal.

    In [46]: (df+df).equals(df*2)
    Out[46]: True

Note that the Series or DataFrame index needs to be in the same order for equality to be True:

    In [47]: df1 = pd.DataFrame({'col':['foo', 0, np.nan]})

    In [48]: df2 = pd.DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])

    In [49]: df1.equals(df2)
    Out[49]: False

    In [50]: df1.equals(df2.sort_index())
    Out[50]: True

### Comparing array-like objects

You can conveniently do element-wise comparisons when comparing a pandas data structure with a scalar value:

    In [51]: pd.Series(['foo', 'bar', 'baz']) == 'foo'
    Out[51]:
    0     True
    1    False
    2    False
    dtype: bool

    In [52]: pd.Index(['foo', 'bar', 'baz']) == 'foo'
    Out[52]: array([ True, False, False], dtype=bool)

Pandas also handles element-wise comparisons between different array-like objects of the same length:

    In [53]: pd.Series(['foo', 'bar', 'baz']) == pd.Index(['foo', 'bar', 'qux'])
    Out[53]:
    0     True
    1     True
    2    False
    dtype: bool

    In [54]: pd.Series(['foo', 'bar', 'baz']) == np.array(['foo', 'bar', 'qux'])
    Out[54]:
    0     True
    1     True
    2    False
    dtype: bool

Trying to compare `Index` or `Series` objects of different lengths will raise a ValueError:

    In [55]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])
    ValueError: Series lengths must match to compare

    In [56]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])
    ValueError: Series lengths must match to compare

Note that this is different from the numpy behavior where a comparison can be broadcast:

    In [55]: np.array([1, 2, 3]) == np.array([2])
    Out[55]: array([False,  True, False], dtype=bool)

or it can return False if broadcasting can not be done:

    In [56]: np.array([1, 2, 3]) == np.array([1, 2])
    Out[56]: False

### Combining overlapping data sets

A problem occasionally arising is the combination of two similar data sets where values in one are preferred over the other. An example would be two data series representing a particular economic indicator where one is considered to be of “higher quality”. However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. The function implementing this operation is `combine_first()`, which we illustrate:

    In [57]: df1 = pd.DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
       ....:                     'B' : [np.nan, 2., 3., np.nan, 6.]})
       ....:

    In [58]: df2 = pd.DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
       ....:                     'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
       ....:

    In [59]: df1
    Out[59]:
        A   B
    0   1 NaN
    1 NaN   2
    2   3   3
    3   5 NaN
    4 NaN   6

    In [60]: df2
    Out[60]:
        A   B
    0   5 NaN
    1   2 NaN
    2   4   3
    3 NaN   4
    4   3   6
    5   7   8

    In [61]: df1.combine_first(df2)
    Out[61]:
       A   B
    0  1 NaN
    1  2   2
    2  3   3
    3  5   4
    4  3   6
    5  7   8

### General DataFrame Combine

The `combine_first()` method above calls the more general DataFrame method `combine()`. This method takes another DataFrame and a combiner function, aligns the input DataFrame and then passes the combiner function pairs of Series (i.e., columns whose names are the same).

So, for instance, to reproduce `combine_first()` as above:

    In [62]: combiner = lambda x, y: np.where(pd.isnull(x), y, x)

    In [63]: df1.combine(df2, combiner)
    Out[63]:
       A   B
    0  1 NaN
    1  2   2
    2  3   3
    3  5   4
    4  3   6
    5  7   8

## Descriptive statistics

A large number of methods for computing descriptive statistics and other related operations on Series, DataFrame, and Panel. Most of these are aggregations (hence producing a lower-dimensional result) like `sum()`, `mean()`, and `quantile()`, but some of them, like `cumsum()` and `cumprod()`, produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, ...}, but the axis can be specified by name or integer:

> - Series: no axis argument needed
> - DataFrame: “index” (axis=0, default), “columns” (axis=1)
> - Panel: “items” (axis=0), “major” (axis=1, default), “minor” (axis=2)

For example:

    In [64]: df
    Out[64]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [65]: df.mean(0)
    Out[65]:
    one     -0.251274
    three    0.469799
    two     -0.191421
    dtype: float64

    In [66]: df.mean(1)
    Out[66]:
    a   -0.489066
    b    0.273355
    c    0.008348
    d    0.011457
    dtype: float64

All such methods have a `skipna` option signaling whether to exclude missing data (`True` by default):

    In [67]: df.sum(0, skipna=False)
    Out[67]:
    one           NaN
    three         NaN
    two     -0.765684
    dtype: float64

    In [68]: df.sum(axis=1, skipna=True)
    Out[68]:
    a   -0.978131
    b    0.820066
    c    0.025044
    d    0.022914
    dtype: float64

Combined with the broadcasting / arithmetic behavior, one can describe various statistical procedures, like standardization (rendering data zero mean and standard deviation 1), very concisely:

    In [69]: ts_stand = (df - df.mean()) / df.std()

    In [70]: ts_stand.std()
    Out[70]:
    one      1
    three    1
    two      1
    dtype: float64

    In [71]: xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)

    In [72]: xs_stand.std(1)
    Out[72]:
    a    1
    b    1
    c    1
    d    1
    dtype: float64

Note that methods like `cumsum()` and `cumprod()` preserve the location of NA values:

    In [73]: df.cumsum()
    Out[73]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.765438 -0.177289  0.784662
    c -0.753821  0.284925  0.335874
    d       NaN  1.409398 -0.765684

Here is a quick reference summary table of common functions. Each also takes an optional `level` parameter which applies only if the object has a hierarchical index.

Function | Description
--- | ---
`count` | Number of non-null observations
`sum` | Sum of values
`mean` | Mean of values
`mad` | Mean absolute deviation
`median` | Arithmetic median of values
`min` | Minimum
`max` | Maximum
`mode` | Mode
`abs` | Absolute Value
`prod` | Product of values
`std` | Unbiased standard deviation
`var` | Unbiased variance
`sem` | Unbiased standard error of the mean
`skew` | Unbiased skewness (3rd moment)
`kurt` | Unbiased kurtosis (4th moment)
`quantile` | Sample quantile (value at %)
`cumsum` | Cumulative sum
`cumprod` | Cumulative product
`cummax` | Cumulative maximum
`cummin` | Cumulative minimum

Note that by chance some NumPy methods, like `mean`, `std`, and `sum`, will exclude NAs on Series input by default:

    In [74]: np.mean(df['one'])
    Out[74]: -0.25127365175839511

    In [75]: np.mean(df['one'].values)
    Out[75]: nan

`Series` also has a method `nunique()` which will return the number of unique non-null values:

    In [76]: series = pd.Series(np.random.randn(500))

    In [77]: series[20:500] = np.nan

    In [78]: series[10:20]  = 5

    In [79]: series.nunique()
    Out[79]: 11

### Summarizing data: describe

There is a convenient `describe()` function which computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course):

    In [80]: series = pd.Series(np.random.randn(1000))

    In [81]: series[::2] = np.nan

    In [82]: series.describe()
    Out[82]:
    count    500.000000
    mean      -0.039663
    std        1.069371
    min       -3.463789
    25%       -0.731101
    50%       -0.058918
    75%        0.672758
    max        3.120271
    dtype: float64

    In [83]: frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])

    In [84]: frame.ix[::2] = np.nan

    In [85]: frame.describe()
    Out[85]:
                    a           b           c           d           e
    count  500.000000  500.000000  500.000000  500.000000  500.000000
    mean     0.000954   -0.044014    0.075936   -0.003679    0.020751
    std      1.005133    0.974882    0.967432    1.004732    0.963812
    min     -3.010899   -2.782760   -3.401252   -2.944925   -3.794127
    25%     -0.682900   -0.681161   -0.528190   -0.663503   -0.615717
    50%     -0.001651   -0.006279    0.040098   -0.003378    0.006282
    75%      0.656439    0.632852    0.717919    0.687214    0.653423
    max      3.007143    2.627688    2.702490    2.850852    3.072117

You can select specific percentiles to include in the output:

    In [86]: series.describe(percentiles=[.05, .25, .75, .95])
    Out[86]:
    count    500.000000
    mean      -0.039663
    std        1.069371
    min       -3.463789
    5%        -1.741334
    25%       -0.731101
    50%       -0.058918
    75%        0.672758
    95%        1.854383
    max        3.120271
    dtype: float64

By default, the median is always included.

For a non-numerical Series object, `describe()` will give a simple summary of the number of unique values and most frequently occurring values:

    In [87]: s = pd.Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])

    In [88]: s.describe()
    Out[88]:
    count     9
    unique    4
    top       a
    freq      5
    dtype: object

Note that on a mixed-type DataFrame object, `describe()` will restrict the summary to include only numerical columns or, if none are, only categorical columns:

    In [89]: frame = pd.DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})

    In [90]: frame.describe()
    Out[90]:
                  b
    count  4.000000
    mean   1.500000
    std    1.290994
    min    0.000000
    25%    0.750000
    50%    1.500000
    75%    2.250000
    max    3.000000

This behaviour can be controlled by providing a list of types as `include`/`exclude` arguments. The special value `all` can also be used:

    In [91]: frame.describe(include=['object'])
    Out[91]:
             a
    count    4
    unique   2
    top     No
    freq     2

    In [92]: frame.describe(include=['number'])
    Out[92]:
                  b
    count  4.000000
    mean   1.500000
    std    1.290994
    min    0.000000
    25%    0.750000
    50%    1.500000
    75%    2.250000
    max    3.000000

    In [93]: frame.describe(include='all')
    Out[93]:
              a         b
    count     4  4.000000
    unique    2       NaN
    top      No       NaN
    freq      2       NaN
    mean    NaN  1.500000
    std     NaN  1.290994
    min     NaN  0.000000
    25%     NaN  0.750000
    50%     NaN  1.500000
    75%     NaN  2.250000
    max     NaN  3.000000

That feature relies on select_dtypes. Refer to there for details about accepted inputs.

### Index of Min/Max Values

The `idxmin()` and `idxmax()` functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:

    In [94]: s1 = pd.Series(np.random.randn(5))

    In [95]: s1
    Out[95]:
    0   -0.872725
    1    1.522411
    2    0.080594
    3   -1.676067
    4    0.435804
    dtype: float64

    In [96]: s1.idxmin(), s1.idxmax()
    Out[96]: (3, 1)

    In [97]: df1 = pd.DataFrame(np.random.randn(5,3), columns=['A','B','C'])

    In [98]: df1
    Out[98]:
              A         B         C
    0  0.445734 -1.649461  0.169660
    1  1.246181  0.131682 -2.001988
    2 -1.273023  0.870502  0.214583
    3  0.088452 -0.173364  1.207466
    4  0.546121  0.409515 -0.310515

    In [99]: df1.idxmin(axis=0)
    Out[99]:
    A    2
    B    0
    C    1
    dtype: int64

    In [100]: df1.idxmax(axis=1)
    Out[100]:
    0    A
    1    A
    2    B
    3    C
    4    A
    dtype: object

When there are multiple rows (or columns) matching the minimum or maximum value, `idxmin()` and `idxmax()` return the first matching index:

    In [101]: df3 = pd.DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))

    In [102]: df3
    Out[102]:
        A
    e   2
    d   1
    c   1
    b   3
    a NaN

    In [103]: df3['A'].idxmin()
    Out[103]: 'd'

Note

`idxmin` and `idxmax` are called `argmin` and `argmax` in NumPy.

### Value counts (histogramming) / Mode

The `value_counts()` Series method and top-level function computes a histogram of a 1D array of values. It can also be used as a function on regular arrays:

    In [104]: data = np.random.randint(0, 7, size=50)

    In [105]: data
    Out[105]:
    array([5, 3, 2, 2, 1, 4, 0, 4, 0, 2, 0, 6, 4, 1, 6, 3, 3, 0, 2, 1, 0, 5, 5,
           3, 6, 1, 5, 6, 2, 0, 0, 6, 3, 3, 5, 0, 4, 3, 3, 3, 0, 6, 1, 3, 5, 5,
           0, 4, 0, 6])

    In [106]: s = pd.Series(data)

    In [107]: s.value_counts()
    Out[107]:
    0    11
    3    10
    6     7
    5     7
    4     5
    2     5
    1     5
    dtype: int64

    In [108]: pd.value_counts(data)
    Out[108]:
    0    11
    3    10
    6     7
    5     7
    4     5
    2     5
    1     5
    dtype: int64

Similarly, you can get the most frequently occurring value(s) (the mode) of the values in a Series or DataFrame:

    In [109]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])

    In [110]: s5.mode()
    Out[110]:
    0    3
    1    7
    dtype: int64

    In [111]: df5 = pd.DataFrame({"A": np.random.randint(0, 7, size=50),
       .....:                     "B": np.random.randint(-10, 15, size=50)})
       .....:

    In [112]: df5.mode()
    Out[112]:
       A  B
    0  1 -5

### Discretization and quantiling

Continuous values can be discretized using the `cut()` (bins based on values) and `qcut()` (bins based on sample quantiles) functions:

    In [113]: arr = np.random.randn(20)

    In [114]: factor = pd.cut(arr, 4)

    In [115]: factor
    Out[115]:
    [(-0.645, 0.336], (-2.61, -1.626], (-1.626, -0.645], (-1.626, -0.645], (-1.626, -0.645], ..., (0.336, 1.316], (0.336, 1.316], (0.336, 1.316], (0.336, 1.316], (-2.61, -1.626]]
    Length: 20
    Categories (4, object): [(-2.61, -1.626] < (-1.626, -0.645] < (-0.645, 0.336] < (0.336, 1.316]]

    In [116]: factor = pd.cut(arr, [-5, -1, 0, 1, 5])

    In [117]: factor
    Out[117]:
    [(-1, 0], (-5, -1], (-1, 0], (-5, -1], (-1, 0], ..., (0, 1], (1, 5], (0, 1], (0, 1], (-5, -1]]
    Length: 20
    Categories (4, object): [(-5, -1] < (-1, 0] < (0, 1] < (1, 5]]

`qcut()` computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so:

    In [118]: arr = np.random.randn(30)

    In [119]: factor = pd.qcut(arr, [0, .25, .5, .75, 1])

    In [120]: factor
    Out[120]:
    [(-0.139, 1.00736], (1.00736, 1.976], (1.00736, 1.976], [-1.0705, -0.439], [-1.0705, -0.439], ..., (1.00736, 1.976], [-1.0705, -0.439], (-0.439, -0.139], (-0.439, -0.139], (-0.439, -0.139]]
    Length: 30
    Categories (4, object): [[-1.0705, -0.439] < (-0.439, -0.139] < (-0.139, 1.00736] < (1.00736, 1.976]]

    In [121]: pd.value_counts(factor)
    Out[121]:
    (1.00736, 1.976]     8
    [-1.0705, -0.439]    8
    (-0.139, 1.00736]    7
    (-0.439, -0.139]     7
    dtype: int64

We can also pass infinite values to define the bins:

    In [122]: arr = np.random.randn(20)

    In [123]: factor = pd.cut(arr, [-np.inf, 0, np.inf])

    In [124]: factor
    Out[124]:
    [(-inf, 0], (0, inf], (0, inf], (0, inf], (-inf, 0], ..., (-inf, 0], (0, inf], (-inf, 0], (-inf, 0], (0, inf]]
    Length: 20
    Categories (2, object): [(-inf, 0] < (0, inf]]

## Function application

To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects to operate on an entire `DataFrame` or `Series`, row- or column-wise, or elementwise.

1. Tablewise Function Application: `pipe()`
2. Row or Column-wise Function Application: `apply()`
3. Elementwise function application: `applymap()`

### Tablewise Function Application

New in version 0.16.2.

`DataFrames` and `Series` can of course just be passed into functions. However, if the function needs to be called in a chain, consider using the `pipe()` method. Compare the following

    # f, g, and h are functions taking and returning ``DataFrames``
    >>> f(g(h(df), arg1=1), arg2=2, arg3=3)

with the equivalent

    >>> (df.pipe(h)
           .pipe(g, arg1=1)
           .pipe(f, arg2=2, arg3=3)
        )

Pandas encourages the second style, which is known as method chaining. `pipe` makes it easy to use your own or another library’s functions in method chains, alongside pandas’ methods.

In the example above, the functions `f`, `g`, and `h` each expected the `DataFrame` as the first positional argument. What if the function you wish to apply takes its data as, say, the second argument? In this case, provide `pipe` with a tuple of `(callable, data_keyword)`. `.pipe` will route the `DataFrame` to the argument specified in the tuple.

For example, we can fit a regression using statsmodels. Their API expects a formula first and a `DataFrame` as the second argument, `data`. We pass in the function, keyword pair `(sm.poisson, 'data')` to `pipe`:

    In [125]: import statsmodels.formula.api as sm

    In [126]: bb = pd.read_csv('data/baseball.csv', index_col='id')

    In [127]: (bb.query('h > 0')
       .....:    .assign(ln_h = lambda df: np.log(df.h))
       .....:    .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)')
       .....:    .fit()
       .....:    .summary()
       .....: )
       .....:
    Optimization terminated successfully.
             Current function value: 2.116284
             Iterations 24
    Out[127]:
    <class 'statsmodels.iolib.summary.Summary'>
    """
                              Poisson Regression Results
    ==============================================================================
    Dep. Variable:                     hr   No. Observations:                   68
    Model:                        Poisson   Df Residuals:                       63
    Method:                           MLE   Df Model:                            4
    Date:                Tue, 12 Apr 2016   Pseudo R-squ.:                  0.6878
    Time:                        19:51:42   Log-Likelihood:                -143.91
    converged:                       True   LL-Null:                       -460.91
                                            LLR p-value:                6.774e-136
    ===============================================================================
                      coef    std err          z      P>|z|      [95.0% Conf. Int.]
    -------------------------------------------------------------------------------
    Intercept   -1267.3636    457.867     -2.768      0.006     -2164.767  -369.960
    C(lg)[T.NL]    -0.2057      0.101     -2.044      0.041        -0.403    -0.008
    ln_h            0.9280      0.191      4.866      0.000         0.554     1.302
    year            0.6301      0.228      2.762      0.006         0.183     1.077
    g               0.0099      0.004      2.754      0.006         0.003     0.017
    ===============================================================================
    """

The pipe method is inspired by unix pipes and more recently dplyr and magrittr, which have introduced the popular `(%>%)` (read pipe) operator for R. The implementation of `pipe` here is quite clean and feels right at home in python. We encourage you to view the source code (`pd.DataFrame.pipe??` in IPython).

### Row or Column-wise Function Application

Arbitrary functions can be applied along the axes of a DataFrame or Panel using the `apply()` method, which, like the descriptive statistics methods, take an optional `axis` argument:

    In [128]: df.apply(np.mean)
    Out[128]:
    one     -0.251274
    three    0.469799
    two     -0.191421
    dtype: float64

    In [129]: df.apply(np.mean, axis=1)
    Out[129]:
    a   -0.489066
    b    0.273355
    c    0.008348
    d    0.011457
    dtype: float64

    In [130]: df.apply(lambda x: x.max() - x.min())
    Out[130]:
    one      0.638161
    three    1.301762
    two      2.237808
    dtype: float64

    In [131]: df.apply(np.cumsum)
    Out[131]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.765438 -0.177289  0.784662
    c -0.753821  0.284925  0.335874
    d       NaN  1.409398 -0.765684

    In [132]: df.apply(np.exp)
    Out[132]:
            one     three       two
    a  0.534436       NaN  0.703570
    b  0.870320  0.837537  3.115063
    c  1.011685  1.587586  0.638401
    d       NaN  3.078592  0.332353

Depending on the return type of the function passed to `apply()`, the result will either be of lower dimension or the same dimension.

`apply()` combined with some cleverness can be used to answer many questions about a data set. For example, suppose we wanted to extract the date where the maximum value for each column occurred:

    In [133]: tsdf = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'],
       .....:                     index=pd.date_range('1/1/2000', periods=1000))
       .....:

    In [134]: tsdf.apply(lambda x: x.idxmax())
    Out[134]:
    A   2001-04-27
    B   2002-06-02
    C   2000-04-02
    dtype: datetime64[ns]

You may also pass additional arguments and keyword arguments to the `apply()` method. For instance, consider the following function you would like to apply:

    def subtract_and_divide(x, sub, divide=1):
        return (x - sub) / divide

You may then apply this function as follows:

    df.apply(subtract_and_divide, args=(5,), divide=3)

Another useful feature is the ability to pass Series methods to carry out some Series operation on each column or row:

    In [135]: tsdf
    Out[135]:
                       A         B         C
    2000-01-01  1.796883 -0.930690  3.542846
    2000-01-02 -1.242888 -0.695279 -1.000884
    2000-01-03 -0.720299  0.546303 -0.082042
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08 -0.527402  0.933507  0.129646
    2000-01-09 -0.338903 -1.265452 -1.969004
    2000-01-10  0.532566  0.341548  0.150493

    In [136]: tsdf.apply(pd.Series.interpolate)
    Out[136]:
                       A         B         C
    2000-01-01  1.796883 -0.930690  3.542846
    2000-01-02 -1.242888 -0.695279 -1.000884
    2000-01-03 -0.720299  0.546303 -0.082042
    2000-01-04 -0.681720  0.623743 -0.039704
    2000-01-05 -0.643140  0.701184  0.002633
    2000-01-06 -0.604561  0.778625  0.044971
    2000-01-07 -0.565982  0.856066  0.087309
    2000-01-08 -0.527402  0.933507  0.129646
    2000-01-09 -0.338903 -1.265452 -1.969004
    2000-01-10  0.532566  0.341548  0.150493

Finally, `apply()` takes an argument `raw` which is False by default, which converts each row or column into a Series before applying the function. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality.

See also

The section on GroupBy demonstrates related, flexible functionality for grouping by some criterion, applying, and combining the results into a Series, DataFrame, etc.

### Applying elementwise Python functions

Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods `applymap()` on DataFrame and analogously `map()` on Series accept any Python function taking a single value and returning a single value. For example:

    In [137]: df4
    Out[137]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [138]: f = lambda x: len(str(x))

    In [139]: df4['one'].map(f)
    Out[139]:
    a    14
    b    15
    c    15
    d     3
    Name: one, dtype: int64

    In [140]: df4.applymap(f)
    Out[140]:
       one  three  two
    a   14      3   15
    b   15     15   11
    c   15     14   15
    d    3     13   14

`Series.map()` has an additional feature which is that it can be used to easily “link” or “map” values defined by a secondary series. This is closely related to merging/joining functionality:

    In [141]: s = pd.Series(['six', 'seven', 'six', 'seven', 'six'],
       .....:               index=['a', 'b', 'c', 'd', 'e'])
       .....:

    In [142]: t = pd.Series({'six' : 6., 'seven' : 7.})

    In [143]: s
    Out[143]:
    a      six
    b    seven
    c      six
    d    seven
    e      six
    dtype: object

    In [144]: s.map(t)
    Out[144]:
    a    6
    b    7
    c    6
    d    7
    e    6
    dtype: float64

### Applying with a Panel

Applying with a `Panel` will pass a `Series` to the applied function. If the applied function returns a `Series`, the result of the application will be a `Panel`. If the applied function reduces to a scalar, the result of the application will be a `DataFrame`.

Note

Prior to 0.13.1 `apply` on a `Panel` would only work on `ufuncs` (e.g. `np.sum/np.max`).

    In [145]: import pandas.util.testing as tm

    In [146]: panel = tm.makePanel(5)

    In [147]: panel
    Out[147]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: A to D

    In [148]: panel['ItemA']
    Out[148]:
                       A         B         C         D
    2000-01-03  0.330418  1.893177  0.801111  0.528154
    2000-01-04  1.761200  0.170247  0.445614 -0.029371
    2000-01-05  0.567133 -0.916844  1.453046 -0.631117
    2000-01-06 -0.251020  0.835024  2.430373 -0.172441
    2000-01-07  1.020099  1.259919  0.653093 -1.020485

A transformational apply.

    In [149]: result = panel.apply(lambda x: x*2, axis='items')

    In [150]: result
    Out[150]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: A to D

    In [151]: result['ItemA']
    Out[151]:
                       A         B         C         D
    2000-01-03  0.660836  3.786354  1.602222  1.056308
    2000-01-04  3.522400  0.340494  0.891228 -0.058742
    2000-01-05  1.134266 -1.833689  2.906092 -1.262234
    2000-01-06 -0.502039  1.670047  4.860747 -0.344882
    2000-01-07  2.040199  2.519838  1.306185 -2.040969

A reduction operation.

    In [152]: panel.apply(lambda x: x.dtype, axis='items')
    Out[152]:
                      A        B        C        D
    2000-01-03  float64  float64  float64  float64
    2000-01-04  float64  float64  float64  float64
    2000-01-05  float64  float64  float64  float64
    2000-01-06  float64  float64  float64  float64
    2000-01-07  float64  float64  float64  float64

A similar reduction type operation

    In [153]: panel.apply(lambda x: x.sum(), axis='major_axis')
    Out[153]:
          ItemA     ItemB     ItemC
    A  3.427831 -2.581431  0.840809
    B  3.241522 -1.409935 -1.114512
    C  5.783237  0.319672 -0.431906
    D -1.325260 -2.914834  0.857043

This last reduction is equivalent to

    In [154]: panel.sum('major_axis')
    Out[154]:
          ItemA     ItemB     ItemC
    A  3.427831 -2.581431  0.840809
    B  3.241522 -1.409935 -1.114512
    C  5.783237  0.319672 -0.431906
    D -1.325260 -2.914834  0.857043

A transformation operation that returns a `Panel`, but is computing the z-score across the `major_axis`.

    In [155]: result = panel.apply(
       .....:            lambda x: (x-x.mean())/x.std(),
       .....:            axis='major_axis')
       .....:

    In [156]: result
    Out[156]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
    Items axis: ItemA to ItemC
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: A to D

    In [157]: result['ItemA']
    Out[157]:
                       A         B         C         D
    2000-01-03 -0.469761  1.156225 -0.441347  1.341731
    2000-01-04  1.422763 -0.444015 -0.882647  0.398661
    2000-01-05 -0.156654 -1.453694  0.367936 -0.619210
    2000-01-06 -1.238841  0.173423  1.581149  0.156654
    2000-01-07  0.442494  0.568061 -0.625091 -1.277837

Apply can also accept multiple axes in the `axis` argument. This will pass a `DataFrame` of the cross-section to the applied function.

    In [158]: f = lambda x: ((x.T-x.mean(1))/x.std(1)).T

    In [159]: result = panel.apply(f, axis = ['items','major_axis'])

    In [160]: result
    Out[160]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
    Items axis: A to D
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: ItemA to ItemC

    In [161]: result.loc[:,:,'ItemA']
    Out[161]:
                       A         B         C         D
    2000-01-03  0.864236  1.132969  0.557316  0.575106
    2000-01-04  0.795745  0.652527  0.534808 -0.070674
    2000-01-05 -0.310864  0.558627  1.086688 -1.051477
    2000-01-06 -0.001065  0.832460  0.846006  0.043602
    2000-01-07  1.128946  1.152469 -0.218186 -0.891680

This is equivalent to the following

    In [162]: result = pd.Panel(dict([ (ax, f(panel.loc[:,:,ax]))
       .....:                         for ax in panel.minor_axis ]))
       .....:

    In [163]: result
    Out[163]:
    <class 'pandas.core.panel.Panel'>
    Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
    Items axis: A to D
    Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
    Minor_axis axis: ItemA to ItemC

    In [164]: result.loc[:,:,'ItemA']
    Out[164]:
                       A         B         C         D
    2000-01-03  0.864236  1.132969  0.557316  0.575106
    2000-01-04  0.795745  0.652527  0.534808 -0.070674
    2000-01-05 -0.310864  0.558627  1.086688 -1.051477
    2000-01-06 -0.001065  0.832460  0.846006  0.043602
    2000-01-07  1.128946  1.152469 -0.218186 -0.891680

## Reindexing and altering labels

`reindex()` is the fundamental data alignment method in pandas. It is used to implement nearly all other features relying on label-alignment functionality. To reindex means to conform the data to match a given set of labels along a particular axis. This accomplishes several things:

> - Reorders the existing data to match a new set of labels
> - Inserts missing value (NA) markers in label locations where no data for that label existed
> - If specified, fill data for missing labels using logic (highly relevant to working with time series data)

Here is a simple example:

    In [165]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [166]: s
    Out[166]:
    a   -1.010924
    b   -0.672504
    c   -1.139222
    d    0.354653
    e    0.563622
    dtype: float64

    In [167]: s.reindex(['e', 'b', 'f', 'd'])
    Out[167]:
    e    0.563622
    b   -0.672504
    f         NaN
    d    0.354653
    dtype: float64

Here, the `f` label was not contained in the Series and hence appears as `NaN` in the result.

With a DataFrame, you can simultaneously reindex the index and columns:

    In [168]: df
    Out[168]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [169]: df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])
    Out[169]:
          three       two       one
    c  0.462215 -0.448789  0.011617
    f       NaN       NaN       NaN
    b -0.177289  1.136249 -0.138894

For convenience, you may utilize the `reindex_axis()` method, which takes the labels and a keyword `axis` parameter.

Note that the `Index` objects containing the actual axis labels can be shared between objects. So if we have a Series and a DataFrame, the following can be done:

    In [170]: rs = s.reindex(df.index)

    In [171]: rs
    Out[171]:
    a   -1.010924
    b   -0.672504
    c   -1.139222
    d    0.354653
    dtype: float64

    In [172]: rs.index is df.index
    Out[172]: True

This means that the reindexed Series’s index is the same Python object as the DataFrame’s index.

See also

MultiIndex / Advanced Indexing is an even more concise way of doing reindexing.

Note

When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: many operations are faster on pre-aligned data. Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference (because `reindex` has been heavily optimized), but when CPU cycles matter sprinkling a few explicit `reindex` calls here and there can have an impact.

### Reindexing to align with another object

You may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the `reindex_like()` method is available to make this simpler:

    In [173]: df2
    Out[173]:
            one       two
    a -0.626544 -0.351587
    b -0.138894  1.136249
    c  0.011617 -0.448789

    In [174]: df3
    Out[174]:
            one       two
    a -0.375270 -0.463545
    b  0.112379  1.024292
    c  0.262891 -0.560746

    In [175]: df.reindex_like(df2)
    Out[175]:
            one       two
    a -0.626544 -0.351587
    b -0.138894  1.136249
    c  0.011617 -0.448789

### Aligning objects with each other with `align`

The `align()` method is the fastest way to simultaneously align two objects. It supports a `join` argument (related to joining and merging):

> - `join='outer'`: take the union of the indexes (default)
> - `join='left'`: use the calling object’s index
> - `join='right'`: use the passed object’s index
> - `join='inner'`: intersect the indexes

It returns a tuple with both of the reindexed Series:

    In [176]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [177]: s1 = s[:4]

    In [178]: s2 = s[1:]

    In [179]: s1.align(s2)
    Out[179]:
    (a   -0.365106
     b    1.092702
     c   -1.481449
     d    1.781190
     e         NaN
     dtype: float64, a         NaN
     b    1.092702
     c   -1.481449
     d    1.781190
     e   -0.031543
     dtype: float64)

    In [180]: s1.align(s2, join='inner')
    Out[180]:
    (b    1.092702
     c   -1.481449
     d    1.781190
     dtype: float64, b    1.092702
     c   -1.481449
     d    1.781190
     dtype: float64)

    In [181]: s1.align(s2, join='left')
    Out[181]:
    (a   -0.365106
     b    1.092702
     c   -1.481449
     d    1.781190
     dtype: float64, a         NaN
     b    1.092702
     c   -1.481449
     d    1.781190
     dtype: float64)

For DataFrames, the join method will be applied to both the index and the columns by default:

    In [182]: df.align(df2, join='inner')
    Out[182]:
    (        one       two
     a -0.626544 -0.351587
     b -0.138894  1.136249
     c  0.011617 -0.448789,         one       two
     a -0.626544 -0.351587
     b -0.138894  1.136249
     c  0.011617 -0.448789)

You can also pass an `axis` option to only align on the specified axis:

    In [183]: df.align(df2, join='inner', axis=0)
    Out[183]:
    (        one     three       two
     a -0.626544       NaN -0.351587
     b -0.138894 -0.177289  1.136249
     c  0.011617  0.462215 -0.448789,         one       two
     a -0.626544 -0.351587
     b -0.138894  1.136249
     c  0.011617 -0.448789)

If you pass a Series to `DataFrame.align()`, you can choose to align both objects either on the DataFrame’s index or columns using the `axis` argument:

    In [184]: df.align(df2.ix[0], axis=1)
    Out[184]:
    (        one     three       two
     a -0.626544       NaN -0.351587
     b -0.138894 -0.177289  1.136249
     c  0.011617  0.462215 -0.448789
     d       NaN  1.124472 -1.101558, one     -0.626544
     three         NaN
     two     -0.351587
     Name: a, dtype: float64)

### Filling while reindexing

`reindex()` takes an optional parameter `method` which is a filling method chosen from the following table:

Method | Action
--- | ---
pad / ffill | Fill values forward
bfill / backfill | Fill values backward
nearest | Fill from the nearest index value

We illustrate these fill methods on a simple Series:

    In [185]: rng = pd.date_range('1/3/2000', periods=8)

    In [186]: ts = pd.Series(np.random.randn(8), index=rng)

    In [187]: ts2 = ts[[0, 3, 6]]

    In [188]: ts
    Out[188]:
    2000-01-03    0.480993
    2000-01-04    0.604244
    2000-01-05   -0.487265
    2000-01-06    1.990533
    2000-01-07    0.327007
    2000-01-08    1.053639
    2000-01-09   -2.927808
    2000-01-10    0.082065
    Freq: D, dtype: float64

    In [189]: ts2
    Out[189]:
    2000-01-03    0.480993
    2000-01-06    1.990533
    2000-01-09   -2.927808
    dtype: float64

    In [190]: ts2.reindex(ts.index)
    Out[190]:
    2000-01-03    0.480993
    2000-01-04         NaN
    2000-01-05         NaN
    2000-01-06    1.990533
    2000-01-07         NaN
    2000-01-08         NaN
    2000-01-09   -2.927808
    2000-01-10         NaN
    Freq: D, dtype: float64

    In [191]: ts2.reindex(ts.index, method='ffill')
    Out[191]:
    2000-01-03    0.480993
    2000-01-04    0.480993
    2000-01-05    0.480993
    2000-01-06    1.990533
    2000-01-07    1.990533
    2000-01-08    1.990533
    2000-01-09   -2.927808
    2000-01-10   -2.927808
    Freq: D, dtype: float64

    In [192]: ts2.reindex(ts.index, method='bfill')
    Out[192]:
    2000-01-03    0.480993
    2000-01-04    1.990533
    2000-01-05    1.990533
    2000-01-06    1.990533
    2000-01-07   -2.927808
    2000-01-08   -2.927808
    2000-01-09   -2.927808
    2000-01-10         NaN
    Freq: D, dtype: float64

    In [193]: ts2.reindex(ts.index, method='nearest')
    Out[193]:
    2000-01-03    0.480993
    2000-01-04    0.480993
    2000-01-05    1.990533
    2000-01-06    1.990533
    2000-01-07    1.990533
    2000-01-08   -2.927808
    2000-01-09   -2.927808
    2000-01-10   -2.927808
    Freq: D, dtype: float64

These methods require that the indexes are ordered increasing or decreasing.

Note that the same result could have been achieved using fillna (except for `method='nearest'`) or interpolate:

    In [194]: ts2.reindex(ts.index).fillna(method='ffill')
    Out[194]:
    2000-01-03    0.480993
    2000-01-04    0.480993
    2000-01-05    0.480993
    2000-01-06    1.990533
    2000-01-07    1.990533
    2000-01-08    1.990533
    2000-01-09   -2.927808
    2000-01-10   -2.927808
    Freq: D, dtype: float64

`reindex()` will raise a ValueError if the index is not monotonic increasing or descreasing. `fillna()` and `interpolate()` will not make any checks on the order of the index.

### Limits on filling while reindexing

The `limit` and `tolerance` arguments provide additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches:

    In [195]: ts2.reindex(ts.index, method='ffill', limit=1)
    Out[195]:
    2000-01-03    0.480993
    2000-01-04    0.480993
    2000-01-05         NaN
    2000-01-06    1.990533
    2000-01-07    1.990533
    2000-01-08         NaN
    2000-01-09   -2.927808
    2000-01-10   -2.927808
    Freq: D, dtype: float64

In contrast, tolerance specifies the maximum distance between the index and indexer values:

    In [196]: ts2.reindex(ts.index, method='ffill', tolerance='1 day')
    Out[196]:
    2000-01-03    0.480993
    2000-01-04    0.480993
    2000-01-05         NaN
    2000-01-06    1.990533
    2000-01-07    1.990533
    2000-01-08         NaN
    2000-01-09   -2.927808
    2000-01-10   -2.927808
    Freq: D, dtype: float64

Notice that when used on a `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`, `tolerance` will coerced into a `Timedelta` if possible. This allows you to specify tolerance with appropriate strings.

### Dropping labels from an axis

A method closely related to `reindex` is the `drop()` function. It removes a set of labels from an axis:

    In [197]: df
    Out[197]:
            one     three       two
    a -0.626544       NaN -0.351587
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789
    d       NaN  1.124472 -1.101558

    In [198]: df.drop(['a', 'd'], axis=0)
    Out[198]:
            one     three       two
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789

    In [199]: df.drop(['one'], axis=1)
    Out[199]:
          three       two
    a       NaN -0.351587
    b -0.177289  1.136249
    c  0.462215 -0.448789
    d  1.124472 -1.101558

Note that the following also works, but is a bit less obvious / clean:

    In [200]: df.reindex(df.index.difference(['a', 'd']))
    Out[200]:
            one     three       two
    b -0.138894 -0.177289  1.136249
    c  0.011617  0.462215 -0.448789

### Renaming / mapping labels

The `rename()` method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.

    In [201]: s
    Out[201]:
    a   -0.365106
    b    1.092702
    c   -1.481449
    d    1.781190
    e   -0.031543
    dtype: float64

    In [202]: s.rename(str.upper)
    Out[202]:
    A   -0.365106
    B    1.092702
    C   -1.481449
    D    1.781190
    E   -0.031543
    dtype: float64

If you pass a function, it must return a value when called with any of the labels (and must produce a set of unique values). But if you pass a dict or Series, it need only contain a subset of the labels as keys:

    In [203]: df.rename(columns={'one' : 'foo', 'two' : 'bar'},
       .....:           index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'})
       .....:
    Out[203]:
                 foo     three       bar
    apple  -0.626544       NaN -0.351587
    banana -0.138894 -0.177289  1.136249
    c       0.011617  0.462215 -0.448789
    durian       NaN  1.124472 -1.101558

The `rename()` method also provides an `inplace` named parameter that is by default `False` and copies the underlying data. Pass `inplace=True` to rename the data in place.

The Panel class has a related `rename_axis()` class which can rename any of its three axes.

## Iteration

The behavior of basic iteration over pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Other data structures, like DataFrame and Panel, follow the dict-like convention of iterating over the “keys” of the objects.

In short, basic iteration (`for i in object`) produces:

- Series: values
- DataFrame: column labels
- Panel: item labels

Thus, for example, iterating over a DataFrame gives you the column names:

    In [204]: df = pd.DataFrame({'col1' : np.random.randn(3), 'col2' : np.random.randn(3)},
       .....:                   index=['a', 'b', 'c'])
       .....:

    In [205]: for col in df:
       .....:     print(col)
       .....:
    col1
    col2

Pandas objects also have the dict-like `iteritems()` method to iterate over the (key, value) pairs.

To iterate over the rows of a DataFrame, you can use the following methods:

- `iterrows()`: Iterate over the rows of a DataFrame as (index, Series) pairs. This converts the rows to Series objects, which can change the dtypes and has some performance implications.
- `itertuples()`: Iterate over the rows of a DataFrame as namedtuples of the values. This is a lot faster as `iterrows()`, and is in most cases preferable to use to iterate over the values of a DataFrame.

Warning

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches:

- Look for a vectorized solution: many operations can be performed using built-in methods or numpy functions, (boolean) indexing, ...
- When you have a function that cannot work on the full DataFrame/Series at once, it is better to use `apply()` instead of iterating over the values. See the docs on function application.
- If you need to do iterative manipulations on the values but performance is important, consider writing the inner loop using e.g. cython or numba. See the enhancing performance section for some examples of this approach.

Warning

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect!

For example, in the following case setting the value has no effect:

    In [206]: df = pd.DataFrame({'a': [1, 2, 3], 'b': ['a', 'b', 'c']})

    In [207]: for index, row in df.iterrows():
       .....:     row['a'] = 10
       .....:

    In [208]: df
    Out[208]:
       a  b
    0  1  a
    1  2  b
    2  3  c

### iteritems

Consistent with the dict-like interface, `iteritems()` iterates through key-value pairs:

- Series: (index, scalar value) pairs
- DataFrame: (column, Series) pairs
- Panel: (item, DataFrame) pairs

For example:

    In [209]: for item, frame in wp.iteritems():
       .....:     print(item)
       .....:     print(frame)
       .....:
    Item1
                       A         B         C         D
    2000-01-01 -1.032011  0.969818 -0.962723  1.382083
    2000-01-02 -0.938794  0.669142 -0.433567 -0.273610
    2000-01-03  0.680433 -0.308450 -0.276099 -1.821168
    2000-01-04 -1.993606 -1.927385 -2.027924  1.624972
    2000-01-05  0.551135  3.059267  0.455264 -0.030740
    Item2
                       A         B         C         D
    2000-01-01  0.935716  1.061192 -2.107852  0.199905
    2000-01-02  0.323586 -0.641630 -0.587514  0.053897
    2000-01-03  0.194889 -0.381994  0.318587  2.089075
    2000-01-04 -0.728293 -0.090255 -0.748199  1.318931
    2000-01-05 -2.029766  0.792652  0.461007 -0.542749

### iterrows

`iterrows()` allows you to iterate through the rows of a DataFrame as Series objects. It returns an iterator yielding each index value along with a Series containing the data in each row:

    In [210]: for row_index, row in df.iterrows():
       .....:     print('%s\n%s' % (row_index, row))
       .....:
    0
    a    1
    b    a
    Name: 0, dtype: object
    1
    a    2
    b    b
    Name: 1, dtype: object
    2
    a    3
    b    c
    Name: 2, dtype: object

Note

Because `iterrows()` returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

    In [211]: df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])

    In [212]: df_orig.dtypes
    Out[212]:
    int        int64
    float    float64
    dtype: object

    In [213]: row = next(df_orig.iterrows())[1]

    In [214]: row
    Out[214]:
    int      1.0
    float    1.5
    Name: 0, dtype: float64

All values in `row`, returned as a Series, are now upcasted to floats, also the original integer value in column x:

    In [215]: row['int'].dtype
    Out[215]: dtype('float64')

    In [216]: df_orig['int'].dtype
    Out[216]: dtype('int64')

To preserve dtypes while iterating over the rows, it is better to use `itertuples()` which returns namedtuples of the values and which is generally much faster as `iterrows`.

For instance, a contrived way to transpose the DataFrame would be:

    In [217]: df2 = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})

    In [218]: print(df2)
       x  y
    0  1  4
    1  2  5
    2  3  6

    In [219]: print(df2.T)
       0  1  2
    x  1  2  3
    y  4  5  6

    In [220]: df2_t = pd.DataFrame(dict((idx,values) for idx, values in df2.iterrows()))

    In [221]: print(df2_t)
       0  1  2
    x  1  2  3
    y  4  5  6

### itertuples

The `itertuples()` method will return an iterator yielding a namedtuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.

For instance,

    In [222]: for row in df.itertuples():
       .....:     print(row)
       .....:
    Pandas(Index=0, a=1, b='a')
    Pandas(Index=1, a=2, b='b')
    Pandas(Index=2, a=3, b='c')

This method does not convert the row to a Series object but just returns the values inside a namedtuple. Therefore, `itertuples()` preserves the data type of the values and is generally faster as `iterrows()`.

Note

The columns names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

## .dt accessor

`Series` has an accessor to succinctly return datetime like properties for the values of the Series, if it is a datetime/period like Series. This will return a Series, indexed like the existing Series.

    # datetime
    In [223]: s = pd.Series(pd.date_range('20130101 09:10:12', periods=4))

    In [224]: s
    Out[224]:
    0   2013-01-01 09:10:12
    1   2013-01-02 09:10:12
    2   2013-01-03 09:10:12
    3   2013-01-04 09:10:12
    dtype: datetime64[ns]

    In [225]: s.dt.hour
    Out[225]:
    0    9
    1    9
    2    9
    3    9
    dtype: int64

    In [226]: s.dt.second
    Out[226]:
    0    12
    1    12
    2    12
    3    12
    dtype: int64

    In [227]: s.dt.day
    Out[227]:
    0    1
    1    2
    2    3
    3    4
    dtype: int64

This enables nice expressions like this:

    In [228]: s[s.dt.day==2]
    Out[228]:
    1   2013-01-02 09:10:12
    dtype: datetime64[ns]

You can easily produces tz aware transformations:

    In [229]: stz = s.dt.tz_localize('US/Eastern')

    In [230]: stz
    Out[230]:
    0   2013-01-01 09:10:12-05:00
    1   2013-01-02 09:10:12-05:00
    2   2013-01-03 09:10:12-05:00
    3   2013-01-04 09:10:12-05:00
    dtype: datetime64[ns, US/Eastern]

    In [231]: stz.dt.tz
    Out[231]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

You can also chain these types of operations:

    In [232]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
    Out[232]:
    0   2013-01-01 04:10:12-05:00
    1   2013-01-02 04:10:12-05:00
    2   2013-01-03 04:10:12-05:00
    3   2013-01-04 04:10:12-05:00
    dtype: datetime64[ns, US/Eastern]

You can also format datetime values as strings with `Series.dt.strftime()` which supports the same format as the standard `strftime()`.

    # DatetimeIndex
    In [233]: s = pd.Series(pd.date_range('20130101', periods=4))

    In [234]: s
    Out[234]:
    0   2013-01-01
    1   2013-01-02
    2   2013-01-03
    3   2013-01-04
    dtype: datetime64[ns]

    In [235]: s.dt.strftime('%Y/%m/%d')
    Out[235]:
    0    2013/01/01
    1    2013/01/02
    2    2013/01/03
    3    2013/01/04
    dtype: object

    # PeriodIndex
    In [236]: s = pd.Series(pd.period_range('20130101', periods=4))

    In [237]: s
    Out[237]:
    0   2013-01-01
    1   2013-01-02
    2   2013-01-03
    3   2013-01-04
    dtype: object

    In [238]: s.dt.strftime('%Y/%m/%d')
    Out[238]:
    0    2013/01/01
    1    2013/01/02
    2    2013/01/03
    3    2013/01/04
    dtype: object

The `.dt` accessor works for period and timedelta dtypes.

    # period
    In [239]: s = pd.Series(pd.period_range('20130101', periods=4, freq='D'))

    In [240]: s
    Out[240]:
    0   2013-01-01
    1   2013-01-02
    2   2013-01-03
    3   2013-01-04
    dtype: object

    In [241]: s.dt.year
    Out[241]:
    0    2013
    1    2013
    2    2013
    3    2013
    dtype: int64

    In [242]: s.dt.day
    Out[242]:
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    # timedelta
    In [243]: s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s'))

    In [244]: s
    Out[244]:
    0   1 days 00:00:05
    1   1 days 00:00:06
    2   1 days 00:00:07
    3   1 days 00:00:08
    dtype: timedelta64[ns]

    In [245]: s.dt.days
    Out[245]:
    0    1
    1    1
    2    1
    3    1
    dtype: int64

    In [246]: s.dt.seconds
    Out[246]:
    0    5
    1    6
    2    7
    3    8
    dtype: int64

    In [247]: s.dt.components
    Out[247]:
       days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
    0     1      0        0        5             0             0            0
    1     1      0        0        6             0             0            0
    2     1      0        0        7             0             0            0
    3     1      0        0        8             0             0            0

Note

`Series.dt` will raise a `TypeError` if you access with a non-datetimelike values

## Vectorized string methods

Series is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series’s `str` attribute and generally have names matching the equivalent (scalar) built-in string methods. For example:

>     In [248]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

>     In [249]: s.str.lower()
>     Out[249]:
>     0       a
>     1       b
>     2       c
>     3    aaba
>     4    baca
>     5     NaN
>     6    caba
>     7     dog
>     8     cat
>     dtype: object

Powerful pattern-matching methods are provided as well, but note that pattern-matching generally uses regular expressions by default (and in some cases always uses them).

Please see Vectorized String Methods for a complete description.

## Sorting

Warning

The sorting API is substantially changed in 0.17.0, see here for these changes. In particular, all sorting methods now return a new object by default, and DO NOT operate in-place (except by passing `inplace=True`).

There are two obvious kinds of sorting that you may be interested in: sorting by label and sorting by actual values.

### By Index

The primary method for sorting axis labels (indexes) are the `Series.sort_index()` and the `DataFrame.sort_index()` methods.

    In [250]: unsorted_df = df.reindex(index=['a', 'd', 'c', 'b'],
       .....:                          columns=['three', 'two', 'one'])
       .....:

    # DataFrame
    In [251]: unsorted_df.sort_index()
    Out[251]:
       three  two  one
    a    NaN  NaN  NaN
    b    NaN  NaN  NaN
    c    NaN  NaN  NaN
    d    NaN  NaN  NaN

    In [252]: unsorted_df.sort_index(ascending=False)
    Out[252]:
       three  two  one
    d    NaN  NaN  NaN
    c    NaN  NaN  NaN
    b    NaN  NaN  NaN
    a    NaN  NaN  NaN

    In [253]: unsorted_df.sort_index(axis=1)
    Out[253]:
       one  three  two
    a  NaN    NaN  NaN
    d  NaN    NaN  NaN
    c  NaN    NaN  NaN
    b  NaN    NaN  NaN

    # Series
    In [254]: unsorted_df['three'].sort_index()
    Out[254]:
    a   NaN
    b   NaN
    c   NaN
    d   NaN
    Name: three, dtype: float64

### By Values

The `Series.sort_values()` and `DataFrame.sort_values()` are the entry points for value sorting (that is the values in a column or row). `DataFrame.sort_values()` can accept an optional `by` argument for `axis=0` which will use an arbitrary vector or a column name of the DataFrame to determine the sort order:

    In [255]: df1 = pd.DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})

    In [256]: df1.sort_values(by='two')
    Out[256]:
       one  three  two
    0    2      5    1
    2    1      3    2
    1    1      4    3
    3    1      2    4

The `by` argument can take a list of column names, e.g.:

    In [257]: df1[['one', 'two', 'three']].sort_values(by=['one','two'])
    Out[257]:
       one  two  three
    2    1    2      3
    1    1    3      4
    3    1    4      2
    0    2    1      5

These methods have special treatment of NA values via the `na_position` argument:

    In [258]: s[2] = np.nan

    In [259]: s.sort_values()
    Out[259]:
    0       A
    3    Aaba
    1       B
    4    Baca
    6    CABA
    8     cat
    7     dog
    2     NaN
    5     NaN
    dtype: object

    In [260]: s.sort_values(na_position='first')
    Out[260]:
    2     NaN
    5     NaN
    0       A
    3    Aaba
    1       B
    4    Baca
    6    CABA
    8     cat
    7     dog
    dtype: object

### searchsorted

Series has the `searchsorted()` method, which works similar to `numpy.ndarray.searchsorted()`.

    In [261]: ser = pd.Series([1, 2, 3])

    In [262]: ser.searchsorted([0, 3])
    Out[262]: array([0, 2])

    In [263]: ser.searchsorted([0, 4])
    Out[263]: array([0, 3])

    In [264]: ser.searchsorted([1, 3], side='right')
    Out[264]: array([1, 3])

    In [265]: ser.searchsorted([1, 3], side='left')
    Out[265]: array([0, 2])

    In [266]: ser = pd.Series([3, 1, 2])

    In [267]: ser.searchsorted([0, 3], sorter=np.argsort(ser))
    Out[267]: array([0, 2])

### smallest / largest values

New in version 0.14.0.

`Series` has the `nsmallest()` and `nlargest()` methods which return the smallest or largest n values. For a large `Series` this can be much faster than sorting the entire Series and calling `head(n)` on the result.

    In [268]: s = pd.Series(np.random.permutation(10))

    In [269]: s
    Out[269]:
    0    9
    1    8
    2    5
    3    3
    4    6
    5    7
    6    0
    7    2
    8    4
    9    1
    dtype: int64

    In [270]: s.sort_values()
    Out[270]:
    6    0
    9    1
    7    2
    3    3
    8    4
    2    5
    4    6
    5    7
    1    8
    0    9
    dtype: int64

    In [271]: s.nsmallest(3)
    Out[271]:
    6    0
    9    1
    7    2
    dtype: int64

    In [272]: s.nlargest(3)
    Out[272]:
    0    9
    1    8
    5    7
    dtype: int64

New in version 0.17.0.

`DataFrame` also has the `nlargest` and `nsmallest` methods.

    In [273]: df = pd.DataFrame({'a': [-2, -1, 1, 10, 8, 11, -1],
       .....:                    'b': list('abdceff'),
       .....:                    'c': [1.0, 2.0, 4.0, 3.2, np.nan, 3.0, 4.0]})
       .....:

    In [274]: df.nlargest(3, 'a')
    Out[274]:
        a  b    c
    5  11  f  3.0
    3  10  c  3.2
    4   8  e  NaN

    In [275]: df.nlargest(5, ['a', 'c'])
    Out[275]:
        a  b    c
    5  11  f  3.0
    3  10  c  3.2
    4   8  e  NaN
    2   1  d  4.0
    1  -1  b  2.0

    In [276]: df.nsmallest(3, 'a')
    Out[276]:
       a  b  c
    0 -2  a  1
    1 -1  b  2
    6 -1  f  4

    In [277]: df.nsmallest(5, ['a', 'c'])
    Out[277]:
       a  b   c
    0 -2  a   1
    1 -1  b   2
    6 -1  f   4
    2  1  d   4
    4  8  e NaN

### Sorting by a multi-index column

You must be explicit about sorting when the column is a multi-index, and fully specify all levels to `by`.

    In [278]: df1.columns = pd.MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])

    In [279]: df1.sort_values(by=('a','two'))
    Out[279]:
        a         b
      one two three
    3   1   2     4
    2   1   3     2
    1   1   4     3
    0   2   5     1

## Copying

The `copy()` method on pandas objects copies the underlying data (though not the axis indexes, since they are immutable) and returns a new object. Note that it is seldom necessary to copy objects. For example, there are only a handful of ways to alter a DataFrame in-place:

> - Inserting, deleting, or modifying a column
> - Assigning to the `index` or `columns` attributes
> - For homogeneous data, directly modifying the values via the `values` attribute or advanced indexing

To be clear, no pandas methods have the side effect of modifying your data; almost all methods return new objects, leaving the original object untouched. If data is modified, it is because you did so explicitly.

## dtypes

The main types stored in pandas objects are `float`, `int`, `bool`, `datetime64[ns]` and `datetime64[ns, tz]` (in >= 0.17.0), `timedelta[ns]`, `category` (in >= 0.15.0), and `object`. In addition these dtypes have item sizes, e.g. `int64` and `int32`. See Series with TZ for more detail on `datetime64[ns, tz]` dtypes.

A convenient `dtypes` attribute for DataFrames returns a Series with the data type of each column.

    In [280]: dft = pd.DataFrame(dict(A = np.random.rand(3),
       .....:                         B = 1,
       .....:                         C = 'foo',
       .....:                         D = pd.Timestamp('20010102'),
       .....:                         E = pd.Series([1.0]*3).astype('float32'),
       .....:                                     F = False,
       .....:                                     G = pd.Series([1]*3,dtype='int8')))
       .....:

    In [281]: dft
    Out[281]:
              A  B    C          D  E      F  G
    0  0.954940  1  foo 2001-01-02  1  False  1
    1  0.318163  1  foo 2001-01-02  1  False  1
    2  0.985803  1  foo 2001-01-02  1  False  1

    In [282]: dft.dtypes
    Out[282]:
    A           float64
    B             int64
    C            object
    D    datetime64[ns]
    E           float32
    F              bool
    G              int8
    dtype: object

On a `Series` use the `dtype` attribute.

    In [283]: dft['A'].dtype
    Out[283]: dtype('float64')

If a pandas object contains data multiple dtypes IN A SINGLE COLUMN, the dtype of the column will be chosen to accommodate all of the data types (`object` is the most general).

    # these ints are coerced to floats
    In [284]: pd.Series([1, 2, 3, 4, 5, 6.])
    Out[284]:
    0    1
    1    2
    2    3
    3    4
    4    5
    5    6
    dtype: float64

    # string data forces an ``object`` dtype
    In [285]: pd.Series([1, 2, 3, 6., 'foo'])
    Out[285]:
    0      1
    1      2
    2      3
    3      6
    4    foo
    dtype: object

The method `get_dtype_counts()` will return the number of columns of each type in a `DataFrame`:

    In [286]: dft.get_dtype_counts()
    Out[286]:
    bool              1
    datetime64[ns]    1
    float32           1
    float64           1
    int64             1
    int8              1
    object            1
    dtype: int64

Numeric dtypes will propagate and can coexist in DataFrames (starting in v0.11.0). If a dtype is passed (either directly via the `dtype` keyword, a passed `ndarray`, or a passed `Series`, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

    In [287]: df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')

    In [288]: df1
    Out[288]:
              A
    0  0.647650
    1  0.822993
    2  1.778703
    3 -1.543048
    4 -0.123256
    5  2.239740
    6 -0.143778
    7 -2.885090

    In [289]: df1.dtypes
    Out[289]:
    A    float32
    dtype: object

    In [290]: df2 = pd.DataFrame(dict( A = pd.Series(np.random.randn(8), dtype='float16'),
       .....:                         B = pd.Series(np.random.randn(8)),
       .....:                         C = pd.Series(np.array(np.random.randn(8), dtype='uint8')) ))
       .....:

    In [291]: df2
    Out[291]:
              A         B    C
    0  0.027588  0.296947    0
    1 -1.150391  0.007045  255
    2  0.246460  0.707877    1
    3 -0.455078  0.950661    0
    4 -1.507812  0.087527    0
    5 -0.502441 -0.339212    0
    6  0.528809 -0.278698    0
    7  0.590332  1.775379    0

    In [292]: df2.dtypes
    Out[292]:
    A    float16
    B    float64
    C      uint8
    dtype: object

### defaults

By default integer types are `int64` and float types are `float64`, REGARDLESS of platform (32-bit or 64-bit). The following will all result in `int64` dtypes.

    In [293]: pd.DataFrame([1, 2], columns=['a']).dtypes
    Out[293]:
    a    int64
    dtype: object

    In [294]: pd.DataFrame({'a': [1, 2]}).dtypes
    Out[294]:
    a    int64
    dtype: object

    In [295]: pd.DataFrame({'a': 1 }, index=list(range(2))).dtypes
    Out[295]:
    a    int64
    dtype: object

Numpy, however will choose platform-dependent types when creating arrays. The following WILL result in `int32` on 32-bit platform.

    In [296]: frame = pd.DataFrame(np.array([1, 2]))

### upcasting

Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (say `int` to `float`)

    In [297]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

    In [298]: df3
    Out[298]:
              A         B    C
    0  0.675238  0.296947    0
    1 -0.327398  0.007045  255
    2  2.025163  0.707877    1
    3 -1.998126  0.950661    0
    4 -1.631068  0.087527    0
    5  1.737299 -0.339212    0
    6  0.385030 -0.278698    0
    7 -2.294758  1.775379    0

    In [299]: df3.dtypes
    Out[299]:
    A    float32
    B    float64
    C    float64
    dtype: object

The `values` attribute on a DataFrame return the lower-common-denominator of the dtypes, meaning the dtype that can accommodate ALL of the types in the resulting homogeneous dtyped numpy array. This can force some upcasting.

    In [300]: df3.values.dtype
    Out[300]: dtype('float64')

### astype

You can use the `astype()` method to explicitly convert dtypes from one to another. These will by default return a copy, even if the dtype was unchanged (pass `copy=False` to change this behavior). In addition, they will raise an exception if the astype operation is invalid.

Upcasting is always according to the numpy rules. If two different dtypes are involved in an operation, then the more general one will be used as the result of the operation.

    In [301]: df3
    Out[301]:
              A         B    C
    0  0.675238  0.296947    0
    1 -0.327398  0.007045  255
    2  2.025163  0.707877    1
    3 -1.998126  0.950661    0
    4 -1.631068  0.087527    0
    5  1.737299 -0.339212    0
    6  0.385030 -0.278698    0
    7 -2.294758  1.775379    0

    In [302]: df3.dtypes
    Out[302]:
    A    float32
    B    float64
    C    float64
    dtype: object

    # conversion of dtypes
    In [303]: df3.astype('float32').dtypes
    Out[303]:
    A    float32
    B    float32
    C    float32
    dtype: object

### object conversion

`convert_objects()` is a method to try to force conversion of types from the `object` dtype to other types. To force conversion of specific types that are number like, e.g. could be a string that represents a number, pass `convert_numeric=True`. This will force strings and numbers alike to be numbers if possible, otherwise they will be set to `np.nan`.

    In [304]: df3['D'] = '1.'

    In [305]: df3['E'] = '1'

    In [306]: df3.convert_objects(convert_numeric=True).dtypes
    Out[306]:
    A    float32
    B    float64
    C    float64
    D    float64
    E      int64
    dtype: object

    # same, but specific dtype conversion
    In [307]: df3['D'] = df3['D'].astype('float16')

    In [308]: df3['E'] = df3['E'].astype('int32')

    In [309]: df3.dtypes
    Out[309]:
    A    float32
    B    float64
    C    float64
    D    float16
    E      int32
    dtype: object

To force conversion to `datetime64[ns]`, pass `convert_dates='coerce'`. This will convert any datetime-like object to dates, forcing other values to `NaT`. This might be useful if you are reading in data which is mostly dates, but occasionally has non-dates intermixed and you want to represent as missing.

    In [310]: import datetime

    In [311]: s = pd.Series([datetime.datetime(2001,1,1,0,0),
       .....:               'foo', 1.0, 1, pd.Timestamp('20010104'),
       .....:               '20010105'], dtype='O')
       .....:

    In [312]: s
    Out[312]:
    0    2001-01-01 00:00:00
    1                    foo
    2                      1
    3                      1
    4    2001-01-04 00:00:00
    5               20010105
    dtype: object

    In [313]: s.convert_objects(convert_dates='coerce')
    Out[313]:
    0   2001-01-01
    1          NaT
    2          NaT
    3          NaT
    4   2001-01-04
    5   2001-01-05
    dtype: datetime64[ns]

In addition, `convert_objects()` will attempt the soft conversion of any object dtypes, meaning that if all the objects in a Series are of the same type, the Series will have that dtype.

### gotchas

Performing selection operations on `integer` type data can easily upcast the data to `floating`. The dtype of the input data will be preserved in cases where `nans` are not introduced (starting in 0.11.0) See also integer na gotchas

    In [314]: dfi = df3.astype('int32')

    In [315]: dfi['E'] = 1

    In [316]: dfi
    Out[316]:
       A  B    C  D  E
    0  0  0    0  1  1
    1  0  0  255  1  1
    2  2  0    1  1  1
    3 -1  0    0  1  1
    4 -1  0    0  1  1
    5  1  0    0  1  1
    6  0  0    0  1  1
    7 -2  1    0  1  1

    In [317]: dfi.dtypes
    Out[317]:
    A    int32
    B    int32
    C    int32
    D    int32
    E    int64
    dtype: object

    In [318]: casted = dfi[dfi>0]

    In [319]: casted
    Out[319]:
        A   B    C  D  E
    0 NaN NaN  NaN  1  1
    1 NaN NaN  255  1  1
    2   2 NaN    1  1  1
    3 NaN NaN  NaN  1  1
    4 NaN NaN  NaN  1  1
    5   1 NaN  NaN  1  1
    6 NaN NaN  NaN  1  1
    7 NaN   1  NaN  1  1

    In [320]: casted.dtypes
    Out[320]:
    A    float64
    B    float64
    C    float64
    D      int32
    E      int64
    dtype: object

While float dtypes are unchanged.

    In [321]: dfa = df3.copy()

    In [322]: dfa['A'] = dfa['A'].astype('float32')

    In [323]: dfa.dtypes
    Out[323]:
    A    float32
    B    float64
    C    float64
    D    float16
    E      int32
    dtype: object

    In [324]: casted = dfa[df2>0]

    In [325]: casted
    Out[325]:
              A         B    C   D   E
    0  0.675238  0.296947  NaN NaN NaN
    1       NaN  0.007045  255 NaN NaN
    2  2.025163  0.707877    1 NaN NaN
    3       NaN  0.950661  NaN NaN NaN
    4       NaN  0.087527  NaN NaN NaN
    5       NaN       NaN  NaN NaN NaN
    6  0.385030       NaN  NaN NaN NaN
    7 -2.294758  1.775379  NaN NaN NaN

    In [326]: casted.dtypes
    Out[326]:
    A    float32
    B    float64
    C    float64
    D    float16
    E    float64
    dtype: object

## Selecting columns based on `dtype`

New in version 0.14.1.

The `select_dtypes()` method implements subsetting of columns based on their `dtype`.

First, let’s create a `DataFrame` with a slew of different dtypes:

    In [327]: df = pd.DataFrame({'string': list('abc'),
       .....:                    'int64': list(range(1, 4)),
       .....:                    'uint8': np.arange(3, 6).astype('u1'),
       .....:                    'float64': np.arange(4.0, 7.0),
       .....:                    'bool1': [True, False, True],
       .....:                    'bool2': [False, True, False],
       .....:                    'dates': pd.date_range('now', periods=3).values,
       .....:                    'category': pd.Series(list("ABC")).astype('category')})
       .....:

    In [328]: df['tdeltas'] = df.dates.diff()

    In [329]: df['uint64'] = np.arange(3, 6).astype('u8')

    In [330]: df['other_dates'] = pd.date_range('20130101', periods=3).values

    In [331]: df['tz_aware_dates'] = pd.date_range('20130101', periods=3, tz='US/Eastern')

    In [332]: df
    Out[332]:
       bool1  bool2 category                      dates  float64  int64 string  \
    0   True  False        A 2016-04-12 19:51:45.683033        4      1      a
    1  False   True        B 2016-04-13 19:51:45.683033        5      2      b
    2   True  False        C 2016-04-14 19:51:45.683033        6      3      c

       uint8  tdeltas  uint64 other_dates            tz_aware_dates
    0      3      NaT       3  2013-01-01 2013-01-01 00:00:00-05:00
    1      4   1 days       4  2013-01-02 2013-01-02 00:00:00-05:00
    2      5   1 days       5  2013-01-03 2013-01-03 00:00:00-05:00

And the dtypes

    In [333]: df.dtypes
    Out[333]:
    bool1                                   bool
    bool2                                   bool
    category                            category
    dates                         datetime64[ns]
    float64                              float64
    int64                                  int64
    string                                object
    uint8                                  uint8
    tdeltas                      timedelta64[ns]
    uint64                                uint64
    other_dates                   datetime64[ns]
    tz_aware_dates    datetime64[ns, US/Eastern]
    dtype: object

`select_dtypes()` has two parameters `include` and `exclude` that allow you to say “give me the columns WITH these dtypes” (`include`) and/or “give the columns WITHOUT these dtypes” (`exclude`).

For example, to select `bool` columns

    In [334]: df.select_dtypes(include=[bool])
    Out[334]:
       bool1  bool2
    0   True  False
    1  False   True
    2   True  False

You can also pass the name of a dtype in the numpy dtype hierarchy:

    In [335]: df.select_dtypes(include=['bool'])
    Out[335]:
       bool1  bool2
    0   True  False
    1  False   True
    2   True  False

`select_dtypes()` also works with generic dtypes as well.

For example, to select all numeric and boolean columns while excluding unsigned integers

    In [336]: df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger'])
    Out[336]:
       bool1  bool2  float64  int64  tdeltas
    0   True  False        4      1      NaT
    1  False   True        5      2   1 days
    2   True  False        6      3   1 days

To select string columns you must use the `object` dtype:

    In [337]: df.select_dtypes(include=['object'])
    Out[337]:
      string
    0      a
    1      b
    2      c

To see all the child dtypes of a generic `dtype` like `numpy.number` you can define a function that returns a tree of child dtypes:

    In [338]: def subdtypes(dtype):
       .....:     subs = dtype.__subclasses__()
       .....:     if not subs:
       .....:         return dtype
       .....:     return [dtype, [subdtypes(dt) for dt in subs]]
       .....:

All numpy dtypes are subclasses of `numpy.generic`:

    In [339]: subdtypes(np.generic)
    Out[339]:
    [numpy.generic,
     [[numpy.number,
       [[numpy.integer,
         [[numpy.signedinteger,
           [numpy.int8,
            numpy.int16,
            numpy.int32,
            numpy.int64,
            numpy.int64,
            numpy.timedelta64]],
          [numpy.unsignedinteger,
           [numpy.uint8,
            numpy.uint16,
            numpy.uint32,
            numpy.uint64,
            numpy.uint64]]]],
        [numpy.inexact,
         [[numpy.floating,
           [numpy.float16, numpy.float32, numpy.float64, numpy.float128]],
          [numpy.complexfloating,
           [numpy.complex64, numpy.complex128, numpy.complex256]]]]]],
      [numpy.flexible,
       [[numpy.character, [numpy.string_, numpy.unicode_]],
        [numpy.void, [numpy.record]]]],
      numpy.bool_,
      numpy.datetime64,
      numpy.object_]]

Note

Pandas also defines the types `category`, and `datetime64[ns, tz]`, which are not integrated into the normal numpy hierarchy and wont show up with the above function.

Note

The `include` and `exclude` parameters must be non-string sequences.