.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   np.set_printoptions(precision=4, suppress=True)
   pd.options.display.max_rows = 15

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:

.. ipython:: python

   index = pd.date_range('1/1/2000', periods=8)
   s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
   df = pd.DataFrame(np.random.randn(8, 3), index=index,
                     columns=['A', 'B', 'C'])
   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 :meth:`~DataFrame.head` and :meth:`~DataFrame.tail` methods. The default number of elements to display is five, but you may pass a custom number.

.. ipython:: python

   long_series = pd.Series(np.random.randn(1000))
   long_series.head()
   long_series.tail(3)

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!

.. ipython:: python

   df[:2]
   df.columns = [x.lower() for x in df.columns]
   df

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

.. ipython:: python

    s.values
    df.values
    wp.values

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 :ref:`Recommended Dependencies <install.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 :meth:`~DataFrame.add`, :meth:`~DataFrame.sub`, :meth:`~DataFrame.mul`, :meth:`~DataFrame.div` and related functions :meth:`~DataFrame.radd`, :meth:`~DataFrame.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:

.. ipython:: python

   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'])})
   df
   row = df.ix[1]
   column = df['two']

   df.sub(row, axis='columns')
   df.sub(row, axis=1)

   df.sub(column, axis='index')
   df.sub(column, axis=0)

.. ipython:: python
   :suppress:

   df_orig = df

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

.. ipython:: python

   dfmi = df.copy()
   dfmi.index = pd.MultiIndex.from_tuples([(1,'a'),(1,'b'),(1,'c'),(2,'a')],
                                          names=['first','second'])
   dfmi.sub(column, axis=0, level='second')

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:

.. ipython:: python

   major_mean = wp.mean(axis='major')
   major_mean
   wp.sub(major_mean, axis='major')

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).

.. ipython:: python
   :suppress:

   df2 = df.copy()
   df2['three']['a'] = 1.

.. ipython:: python

   df
   df2
   df + df2
   df.add(df2, fill_value=0)

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:

.. ipython:: python

   df.gt(df2)
   df2.ne(df)

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 :ref:`here<indexing.boolean>`

Boolean Reductions

You can apply the reductions: :attr:`~DataFrame.empty`, :meth:`~DataFrame.any`, :meth:`~DataFrame.all`, and :meth:`~DataFrame.bool` to provide a way to summarize a boolean result.

.. ipython:: python

   (df > 0).all()
   (df > 0).any()

You can reduce to a final boolean value.

.. ipython:: python

   (df > 0).any().any()

You can test if a pandas object is empty, via the :attr:`~DataFrame.empty` property.

.. ipython:: python

   df.empty
   pd.DataFrame(columns=list('ABC')).empty

To evaluate single-element pandas objects in a boolean context, use the method :meth:`~DataFrame.bool`:

.. ipython:: python

   pd.Series([True]).bool()
   pd.Series([False]).bool()
   pd.DataFrame([[True]]).bool()
   pd.DataFrame([[False]]).bool()

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 :ref:`gotchas<gotchas.truth>` 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:

.. ipython:: python

   df+df == df*2
   (df+df == df*2).all()

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

.. ipython:: python

   np.nan == np.nan

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

.. ipython:: python

   (df+df).equals(df*2)

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

.. ipython:: python

   df1 = pd.DataFrame({'col':['foo', 0, np.nan]})
   df2 = pd.DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
   df1.equals(df2)
   df1.equals(df2.sort())

Comparing array-like objects

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

.. ipython:: python

   pd.Series(['foo', 'bar', 'baz']) == 'foo'
   pd.Index(['foo', 'bar', 'baz']) == 'foo'

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

.. ipython:: python

    pd.Series(['foo', 'bar', 'baz']) == pd.Index(['foo', 'bar', 'qux'])
    pd.Series(['foo', 'bar', 'baz']) == np.array(['foo', 'bar', 'qux'])

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:

.. ipython:: python

    np.array([1, 2, 3]) == np.array([2])

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

.. ipython:: python

    np.array([1, 2, 3]) == np.array([1, 2])

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 :meth:`~DataFrame.combine_first`, which we illustrate:

.. ipython:: python

   df1 = pd.DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
                       'B' : [np.nan, 2., 3., np.nan, 6.]})
   df2 = pd.DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
                       'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
   df1
   df2
   df1.combine_first(df2)

General DataFrame Combine

The :meth:`~DataFrame.combine_first` method above calls the more general DataFrame method :meth:`~DataFrame.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 :meth:`~DataFrame.combine_first` as above:

.. ipython:: python

   combiner = lambda x, y: np.where(pd.isnull(x), y, x)
   df1.combine(df2, combiner)

Descriptive statistics

A large number of methods for computing descriptive statistics and other related operations on :ref:`Series <api.series.stats>`, :ref:`DataFrame <api.dataframe.stats>`, and :ref:`Panel <api.panel.stats>`. Most of these are aggregations (hence producing a lower-dimensional result) like :meth:`~DataFrame.sum`, :meth:`~DataFrame.mean`, and :meth:`~DataFrame.quantile`, but some of them, like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.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:

.. ipython:: python

   df
   df.mean(0)
   df.mean(1)

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

.. ipython:: python

   df.sum(0, skipna=False)
   df.sum(axis=1, skipna=True)

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

.. ipython:: python

   ts_stand = (df - df.mean()) / df.std()
   ts_stand.std()
   xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)
   xs_stand.std(1)

Note that methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod` preserve the location of NA values:

.. ipython:: python

   df.cumsum()

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 :ref:`hierarchical index<advanced.hierarchical>`.

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:

.. ipython:: python

   np.mean(df['one'])
   np.mean(df['one'].values)

Series also has a method :meth:`~Series.nunique` which will return the number of unique non-null values:

.. ipython:: python

   series = pd.Series(np.random.randn(500))
   series[20:500] = np.nan
   series[10:20]  = 5
   series.nunique()

Summarizing data: describe

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

.. ipython:: python

    series = pd.Series(np.random.randn(1000))
    series[::2] = np.nan
    series.describe()
    frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
    frame.ix[::2] = np.nan
    frame.describe()

You can select specific percentiles to include in the output:

.. ipython:: python

    series.describe(percentiles=[.05, .25, .75, .95])

By default, the median is always included.

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

.. ipython:: python

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

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

.. ipython:: python

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

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

.. ipython:: python

    frame.describe(include=['object'])
    frame.describe(include=['number'])
    frame.describe(include='all')

That feature relies on :ref:`select_dtypes <basics.selectdtypes>`. Refer to there for details about accepted inputs.

Index of Min/Max Values

The :meth:`~DataFrame.idxmin` and :meth:`~DataFrame.idxmax` functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:

.. ipython:: python

   s1 = pd.Series(np.random.randn(5))
   s1
   s1.idxmin(), s1.idxmax()

   df1 = pd.DataFrame(np.random.randn(5,3), columns=['A','B','C'])
   df1
   df1.idxmin(axis=0)
   df1.idxmax(axis=1)

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

.. ipython:: python

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

Note

idxmin and idxmax are called argmin and argmax in NumPy.

Value counts (histogramming) / Mode

The :meth:`~Series.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:

.. ipython:: python

   data = np.random.randint(0, 7, size=50)
   data
   s = pd.Series(data)
   s.value_counts()
   pd.value_counts(data)

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

.. ipython:: python

    s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
    s5.mode()
    df5 = pd.DataFrame({"A": np.random.randint(0, 7, size=50),
                        "B": np.random.randint(-10, 15, size=50)})
    df5.mode()

Discretization and quantiling

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

.. ipython:: python

   arr = np.random.randn(20)
   factor = pd.cut(arr, 4)
   factor

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

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

.. ipython:: python

   arr = np.random.randn(30)
   factor = pd.qcut(arr, [0, .25, .5, .75, 1])
   factor
   pd.value_counts(factor)

We can also pass infinite values to define the bins:

.. ipython:: python

   arr = np.random.randn(20)
   factor = pd.cut(arr, [-np.inf, 0, np.inf])
   factor

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.

Tablewise Function Application: :meth:`~DataFrame.pipe`
Row or Column-wise Function Application: :meth:`~DataFrame.apply`
Elementwise function application: :meth:`~DataFrame.applymap`

Tablewise Function Application

.. versionadded:: 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 :meth:`~DataFrame.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:

.. ipython:: python

   import statsmodels.formula.api as sm

   bb = pd.read_csv('data/baseball.csv', index_col='id')

   (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()
   )

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 :meth:`~DataFrame.apply` method, which, like the descriptive statistics methods, take an optional axis argument:

.. ipython:: python

   df.apply(np.mean)
   df.apply(np.mean, axis=1)
   df.apply(lambda x: x.max() - x.min())
   df.apply(np.cumsum)
   df.apply(np.exp)

Depending on the return type of the function passed to :meth:`~DataFrame.apply`, the result will either be of lower dimension or the same dimension.

:meth:`~DataFrame.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:

.. ipython:: python

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

You may also pass additional arguments and keyword arguments to the :meth:`~DataFrame.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:

.. ipython:: python
   :suppress:

   tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
                       index=pd.date_range('1/1/2000', periods=10))
   tsdf.values[3:7] = np.nan

.. ipython:: python

   tsdf
   tsdf.apply(pd.Series.interpolate)

Finally, :meth:`~DataFrame.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.

.. seealso::

   The section on :ref:`GroupBy <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 :meth:`~DataFrame.applymap` on DataFrame and analogously :meth:`~Series.map` on Series accept any Python function taking a single value and returning a single value. For example:

.. ipython:: python
   :suppress:

   df4 = df_orig.copy()

.. ipython:: python

   df4
   f = lambda x: len(str(x))
   df4['one'].map(f)
   df4.applymap(f)

:meth:`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 :ref:`merging/joining functionality <merging>`:

.. ipython:: python

   s = pd.Series(['six', 'seven', 'six', 'seven', 'six'],
                 index=['a', 'b', 'c', 'd', 'e'])
   t = pd.Series({'six' : 6., 'seven' : 7.})
   s
   s.map(t)

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).

.. ipython:: python

   import pandas.util.testing as tm
   panel = tm.makePanel(5)
   panel
   panel['ItemA']

A transformational apply.

.. ipython:: python

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

A reduction operation.

.. ipython:: python

   panel.apply(lambda x: x.dtype, axis='items')

A similar reduction type operation

.. ipython:: python

   panel.apply(lambda x: x.sum(), axis='major_axis')

This last reduction is equivalent to

.. ipython:: python

   panel.sum('major_axis')

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

.. ipython:: python

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

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

.. ipython:: python

   f = lambda x: ((x.T-x.mean(1))/x.std(1)).T

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

This is equivalent to the following

.. ipython:: python

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

Reindexing and altering labels

:meth:`~Series.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:

.. ipython:: python

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

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:

.. ipython:: python

   df
   df.reindex(index=['c', 'f', 'b'], columns=['three', 'two', 'one'])

For convenience, you may utilize the :meth:`~Series.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:

.. ipython:: python

   rs = s.reindex(df.index)
   rs
   rs.index is df.index

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

.. seealso::

   :ref:`MultiIndex / Advanced Indexing <advanced>` 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 :meth:`~DataFrame.reindex_like` method is available to make this simpler:

.. ipython:: python
   :suppress:

   df2 = df.reindex(['a', 'b', 'c'], columns=['one', 'two'])
   df3 = df2 - df2.mean()

.. ipython:: python

   df2
   df3
   df.reindex_like(df2)

Aligning objects with each other with `align`

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

.. ipython:: python

   s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
   s1 = s[:4]
   s2 = s[1:]
   s1.align(s2)
   s1.align(s2, join='inner')
   s1.align(s2, join='left')

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

.. ipython:: python

   df.align(df2, join='inner')

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

.. ipython:: python

   df.align(df2, join='inner', axis=0)

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

.. ipython:: python

   df.align(df2.ix[0], axis=1)

Filling while reindexing

:meth:`~Series.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:

.. ipython:: python

   rng = pd.date_range('1/3/2000', periods=8)
   ts = pd.Series(np.random.randn(8), index=rng)
   ts2 = ts[[0, 3, 6]]
   ts
   ts2

   ts2.reindex(ts.index)
   ts2.reindex(ts.index, method='ffill')
   ts2.reindex(ts.index, method='bfill')
   ts2.reindex(ts.index, method='nearest')

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

Note that the same result could have been achieved using :ref:`fillna <missing_data.fillna>` (except for method='nearest') or :ref:`interpolate <missing_data.interpolation>`:

.. ipython:: python

   ts2.reindex(ts.index).fillna(method='ffill')

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

Dropping labels from an axis

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

.. ipython:: python

   df
   df.drop(['a', 'd'], axis=0)
   df.drop(['one'], axis=1)

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

.. ipython:: python

   df.reindex(df.index.difference(['a', 'd']))

Renaming / mapping labels

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

.. ipython:: python

   s
   s.rename(str.upper)

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:

.. ipython:: python

   df.rename(columns={'one' : 'foo', 'two' : 'bar'},
             index={'a' : 'apple', 'b' : 'banana', 'd' : 'durian'})

The :meth:`~DataFrame.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 :meth:`~Panel.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:

.. ipython::

    In [0]: df = pd.DataFrame({'col1' : np.random.randn(3), 'col2' : np.random.randn(3)},
       ...:                   index=['a', 'b', 'c'])

    In [0]: for col in df:
       ...:     print(col)
       ...:

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

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

:meth:`~DataFrame.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.
:meth:`~DataFrame.itertuples`: Iterate over the rows of a DataFrame as tuples of the values. This is a lot faster as :meth:`~DataFrame.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 :meth:`~DataFrame.apply` instead of iterating over the values. See the docs on :ref:`function application <basics.apply>`.
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 :ref:`enhancing performance <enhancingperf>` 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:

.. ipython:: python

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

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

  df

iteritems

Consistent with the dict-like interface, :meth:`~DataFrame.iteritems` iterates through key-value pairs:

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

For example:

.. ipython::

   In [0]: for item, frame in wp.iteritems():
      ...:     print(item)
      ...:     print(frame)
      ...:

iterrows

:meth:`~DataFrame.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:

.. ipython::

   In [0]: for row_index, row in df.iterrows():
      ...:     print('%s\n%s' % (row_index, row))
      ...:

Note

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

.. ipython:: python

   df_orig = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
   df_orig.dtypes
   row = next(df_orig.iterrows())[1]
   row

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

.. ipython:: python

   row['int'].dtype
   df_orig['int'].dtype

To preserve dtypes while iterating over the rows, it is better to use :meth:`~DataFrame.itertuples` which returns tuples of the values and which is generally much faster as iterrows.

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

.. ipython:: python

   df2 = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
   print(df2)
   print(df2.T)

   df2_t = pd.DataFrame(dict((idx,values) for idx, values in df2.iterrows()))
   print(df2_t)

itertuples

The :meth:`~DataFrame.itertuples` method will return an iterator yielding a tuple 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,

.. ipython:: python

   for row in df.itertuples():
       print(row)

This method does not convert the row to a Series object but just returns the values inside a tuple. Therefore, :meth:`~DataFrame.itertuples` preserves the data type of the values and is generally faster as :meth:`~DataFrame.iterrows`.

.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.

.. ipython:: python

   # datetime
   s = pd.Series(pd.date_range('20130101 09:10:12', periods=4))
   s
   s.dt.hour
   s.dt.second
   s.dt.day

This enables nice expressions like this:

.. ipython:: python

   s[s.dt.day==2]

You can easily produces tz aware transformations:

.. ipython:: python

   stz = s.dt.tz_localize('US/Eastern')
   stz
   stz.dt.tz

You can also chain these types of operations:

.. ipython:: python

   s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

You can also format datetime values as strings with :meth:`Series.dt.strftime` which supports the same format as the standard :meth:`~datetime.datetime.strftime`.

.. ipython:: python

   # DatetimeIndex
   s = pd.Series(pd.date_range('20130101', periods=4))
   s
   s.dt.strftime('%Y/%m/%d')

.. ipython:: python

   # PeriodIndex
   s = pd.Series(pd.period_range('20130101', periods=4))
   s
   s.dt.strftime('%Y/%m/%d')

The .dt accessor works for period and timedelta dtypes.

.. ipython:: python

   # period
   s = pd.Series(pd.period_range('20130101', periods=4, freq='D'))
   s
   s.dt.year
   s.dt.day

.. ipython:: python

   # timedelta
   s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s'))
   s
   s.dt.days
   s.dt.seconds
   s.dt.components

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:

.. ipython:: python

 s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
 s.str.lower()

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 :ref:`Vectorized String Methods <text.string_methods>` for a complete description.

Sorting

Warning

The sorting API is substantially changed in 0.17.0, see :ref:`here <whatsnew_0170.api_breaking.sorting>` 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.

.. ipython:: python

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

   # DataFrame
   unsorted_df.sort_index()
   unsorted_df.sort_index(ascending=False)
   unsorted_df.sort_index(axis=1)

   # Series
   unsorted_df['three'].sort_index()

By Values

The :meth:`Series.sort_values` and :meth:`DataFrame.sort_values` are the entry points for value sorting (that is the values in a column or row). :meth:`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:

.. ipython:: python

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

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

.. ipython:: python

   df1[['one', 'two', 'three']].sort_index(by=['one','two'])

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

.. ipython:: python

   s[2] = np.nan
   s.order()
   s.order(na_position='first')

searchsorted

Series has the :meth:`~Series.searchsorted` method, which works similar to :meth:`numpy.ndarray.searchsorted`.

.. ipython:: python

   ser = pd.Series([1, 2, 3])
   ser.searchsorted([0, 3])
   ser.searchsorted([0, 4])
   ser.searchsorted([1, 3], side='right')
   ser.searchsorted([1, 3], side='left')
   ser = pd.Series([3, 1, 2])
   ser.searchsorted([0, 3], sorter=np.argsort(ser))

smallest / largest values

.. versionadded:: 0.14.0

Series has the :meth:`~Series.nsmallest` and :meth:`~Series.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.

.. ipython:: python

   s = pd.Series(np.random.permutation(10))
   s
   s.sort_values()
   s.nsmallest(3)
   s.nlargest(3)

.. versionadded:: 0.17.0

DataFrame also has the nlargest and nsmallest methods.

.. ipython:: python

   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]})
   df.nlargest(3, 'a')
   df.nlargest(5, ['a', 'c'])
   df.nsmallest(3, 'a')
   df.nsmallest(5, ['a', 'c'])

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.

.. ipython:: python

   df1.columns = pd.MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
   df1.sort_index(by=('a','two'))

Copying

The :meth:`~DataFrame.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], timedelta[ns] and object. In addition these dtypes have item sizes, e.g. int64 and int32. A convenient :attr:`~DataFrame.dtypes`` attribute for DataFrames returns a Series with the data type of each column.

.. ipython:: python

   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')))
   dft
   dft.dtypes

On a Series use the :attr:`~Series.dtype` attribute.

.. ipython:: python

   dft['A'].dtype

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).

.. ipython:: python

   # these ints are coerced to floats
   pd.Series([1, 2, 3, 4, 5, 6.])

   # string data forces an ``object`` dtype
   pd.Series([1, 2, 3, 6., 'foo'])

The method :meth:`~DataFrame.get_dtype_counts` will return the number of columns of each type in a DataFrame:

.. ipython:: python

   dft.get_dtype_counts()

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.

.. ipython:: python

   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
   df1
   df1.dtypes
   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')) ))
   df2
   df2.dtypes

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.

.. ipython:: python

   pd.DataFrame([1, 2], columns=['a']).dtypes
   pd.DataFrame({'a': [1, 2]}).dtypes
   pd.DataFrame({'a': 1 }, index=list(range(2))).dtypes

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

.. ipython:: python

   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)

.. ipython:: python

   df3 = df1.reindex_like(df2).fillna(value=0.0) + df2
   df3
   df3.dtypes

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.

.. ipython:: python

   df3.values.dtype

astype

You can use the :meth:`~DataFrame.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.

.. ipython:: python

   df3
   df3.dtypes

   # conversion of dtypes
   df3.astype('float32').dtypes

object conversion

Note

The syntax of :meth:`~DataFrame.convert_objects` changed in 0.17.0. See :ref:`API changes <whatsnew_0170.api_breaking.convert_objects>` for more details.

:meth:`~DataFrame.convert_objects` is a method that converts columns from the object dtype to datetimes, timedeltas or floats. For example, to attempt conversion of object data that are number like, e.g. could be a string that represents a number, pass numeric=True. By default, this will attempt a soft conversion and so will only succeed if the entire column is convertible. To force the conversion, add the keyword argument coerce=True. This will force strings and number-like objects to be numbers if possible, and other values will be set to np.nan.

.. ipython:: python

   df3['D'] = '1.'
   df3['E'] = '1'
   df3.convert_objects(numeric=True).dtypes

   # same, but specific dtype conversion
   df3['D'] = df3['D'].astype('float16')
   df3['E'] = df3['E'].astype('int32')
   df3.dtypes

To force conversion to datetime64[ns], pass datetime=True and coerce=True. 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 contains non-dates that you wish to represent as missing.

.. ipython:: python

   import datetime
   s = pd.Series([datetime.datetime(2001,1,1,0,0),
                 'foo', 1.0, 1, pd.Timestamp('20010104'),
                 '20010105'], dtype='O')
   s
   s.convert_objects(datetime=True, coerce=True)

Without passing coerce=True, :meth:`~DataFrame.convert_objects` will attempt 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. Note that setting coerce=True does not convert arbitrary types to either datetime64[ns] or timedelta64[ns]. For example, a series containing string dates will not be converted to a series of datetimes. To convert between types, see :ref:`converting to timestamps <timeseries.converting>`.

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 :ref:`integer na gotchas <gotchas.intna>`

.. ipython:: python

   dfi = df3.astype('int32')
   dfi['E'] = 1
   dfi
   dfi.dtypes

   casted = dfi[dfi>0]
   casted
   casted.dtypes

While float dtypes are unchanged.

.. ipython:: python

   dfa = df3.copy()
   dfa['A'] = dfa['A'].astype('float32')
   dfa.dtypes

   casted = dfa[df2>0]
   casted
   casted.dtypes

Selecting columns based on `dtype`

.. versionadded:: 0.14.1

The :meth:`~DataFrame.select_dtypes` method implements subsetting of columns based on their dtype.

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

.. ipython:: python

   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')})
   df['tdeltas'] = df.dates.diff()
   df['uint64'] = np.arange(3, 6).astype('u8')
   df['other_dates'] = pd.date_range('20130101', periods=3).values
   df

:meth:`~DataFrame.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

.. ipython:: python

   df.select_dtypes(include=[bool])

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

.. ipython:: python

   df.select_dtypes(include=['bool'])

:meth:`~pandas.DataFrame.select_dtypes` also works with generic dtypes as well.

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

.. ipython:: python

   df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger'])

To select string columns you must use the object dtype:

.. ipython:: python

   df.select_dtypes(include=['object'])

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:

.. ipython:: python

   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:

.. ipython:: python

    subdtypes(np.generic)

Note

Pandas also defines an additional category dtype, which is 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.

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basics.rst

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Essential Basic Functionality

Head and Tail

Attributes and the raw ndarray(s)

Accelerated operations

Flexible binary operations

Matching / broadcasting behavior

Missing data / operations with fill values

Flexible Comparisons

Boolean Reductions

Comparing if objects are equivalent

Comparing array-like objects

Combining overlapping data sets

General DataFrame Combine

Descriptive statistics

Summarizing data: describe

Index of Min/Max Values

Value counts (histogramming) / Mode

Discretization and quantiling

Function application

Tablewise Function Application

Row or Column-wise Function Application

Applying elementwise Python functions

Applying with a Panel

Reindexing and altering labels

Reindexing to align with another object

Aligning objects with each other with align

Filling while reindexing

Dropping labels from an axis

Renaming / mapping labels

Iteration

iteritems

iterrows

itertuples

.dt accessor

Vectorized string methods

Sorting

By Index

By Values

searchsorted

smallest / largest values

Sorting by a multi-index column

Copying

dtypes

defaults

upcasting

astype

object conversion

gotchas

Selecting columns based on dtype

Aligning objects with each other with `align`

Selecting columns based on `dtype`