Introducing Pandas Objects¶

Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.

Let's introduce these three fundamental Pandas data structures: the Series, DataFrame, and Index.

In [14]:
import numpy as np
import pandas as pd

The Pandas Series Object¶

A Pandas Series is a one-dimensional array of indexed data:

In [15]:
data = pd.Series([0.25, 0.5, 0.75, 1.0])
data
Out[15]:
0    0.25
1    0.50
2    0.75
3    1.00
dtype: float64

Series objects wrap both a sequence of values and a sequence of indices, which we can access with the values and index attributes.

The values are simply a familiar NumPy array:

In [16]:
data.values
Out[16]:
array([0.25, 0.5 , 0.75, 1.  ])
In [17]:
type(_)
Out[17]:
numpy.ndarray

The index is an array-like object of type pd.Index:

In [18]:
data.index
Out[18]:
RangeIndex(start=0, stop=4, step=1)
In [19]:
print(pd.RangeIndex.__doc__)

    Immutable Index implementing a monotonic integer range.

    RangeIndex is a memory-saving special case of Int64Index limited to
    representing monotonic ranges. Using RangeIndex may in some instances
    improve computing speed.

    This is the default index type used
    by DataFrame and Series when no explicit index is provided by the user.

    Parameters
    ----------
    start : int (default: 0), or other RangeIndex instance
        If int and "stop" is not given, interpreted as "stop" instead.
    stop : int (default: 0)
    step : int (default: 1)
    dtype : np.int64
        Unused, accepted for homogeneity with other index types.
    copy : bool, default False
        Unused, accepted for homogeneity with other index types.
    name : object, optional
        Name to be stored in the index.

    Attributes
    ----------
    start
    stop
    step

    Methods
    -------
    from_range

    See Also
    --------
    Index : The base pandas Index type.
    Int64Index : Index of int64 data.

Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:

In [20]:
data[1]
Out[20]:
0.5
In [21]:
data[1:3]
Out[21]:
1    0.50
2    0.75
dtype: float64
In [22]:
type(_)
Out[22]:
pandas.core.series.Series

Series as generalized NumPy array¶

It may look like the Series object is basically interchangeable with a one-dimensional NumPy array. The essential difference is the presence of the index: while the Numpy Array has an implicitly defined integer index used to access the values, the Pandas Series has an explicitly defined index associated with the values.

In [23]:
data = pd.Series([0.25, 0.5, 0.75, 1.0], index=['a', 'b', 'c', 'd'])
data
Out[23]:
a    0.25
b    0.50
c    0.75
d    1.00
dtype: float64
In [24]:
data['b'] # item access works as expected
Out[24]:
0.5

We can even use non-contiguous or non-sequential indices:

In [25]:
data = pd.Series([0.25, 0.5, 0.75, 1.0], index=[2, 5, 3, 7])
data
Out[25]:
2    0.25
5    0.50
3    0.75
7    1.00
dtype: float64
In [11]:
data[5]
Out[11]:
0.5

Series as specialized dictionary¶

You can think of a Pandas Series a bit like a specialization of a Python dictionary. A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a Series is a structure which maps typed keys to a set of typed values:

In [27]:
population_dict = {'California': 38332521,
                   'Texas': 26448193,
                   'New York': 19651127,
                   'Florida': 19552860,
                   'Illinois': 12882135}
population = pd.Series(population_dict)
population
Out[27]:
California    38332521
Texas         26448193
New York      19651127
Florida       19552860
Illinois      12882135
dtype: int64
In [28]:
type(_)
Out[28]:
pandas.core.series.Series
In [29]:
population.index
Out[29]:
Index(['California', 'Texas', 'New York', 'Florida', 'Illinois'], dtype='object')
In [30]:
population['California'] # typical dictionary-style item access
Out[30]:
38332521
In [31]:
population['California':'Illinois'] # array-like slicing
Out[31]:
California    38332521
Texas         26448193
New York      19651127
Florida       19552860
Illinois      12882135
dtype: int64

The Pandas DataFrame Object¶

The next fundamental structure in Pandas is the DataFrame which can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.

DataFrame as a generalized NumPy array¶

If a Series is an analog of a one-dimensional array with flexible indices, a DataFrame is an analog of a two-dimensional array with both flexible row indices and flexible column names.

You can think of a DataFrame as a sequence of aligned Series objects. Here, by aligned we mean that they share the same index:

In [32]:
area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,
             'Florida': 170312, 'Illinois': 149995}
area = pd.Series(area_dict)
area
Out[32]:
California    423967
Texas         695662
New York      141297
Florida       170312
Illinois      149995
dtype: int64
In [36]:
states = pd.DataFrame({'population': population, 'area': area})
states
Out[36]:
population area
California 38332521 423967
Texas 26448193 695662
New York 19651127 141297
Florida 19552860 170312
Illinois 12882135 149995
In [37]:
type(_)
Out[37]:
pandas.core.frame.DataFrame
In [38]:
states.index
Out[38]:
Index(['California', 'Texas', 'New York', 'Florida', 'Illinois'], dtype='object')

Additionally, the DataFrame has a columns attribute, which is an Index object holding the column labels:

In [39]:
states.columns
Out[39]:
Index(['population', 'area'], dtype='object')
In [40]:
type(_)
Out[40]:
pandas.core.indexes.base.Index

Thus the DataFrame can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data.

DataFrame as specialized dictionary¶

Similarly, we can also think of a DataFrame as a specialization of a dictionary.

Where a dictionary maps a key to a value, a DataFrame maps a column name to a Series of column data:

In [41]:
states['area'] # "feature"
Out[41]:
California    423967
Texas         695662
New York      141297
Florida       170312
Illinois      149995
Name: area, dtype: int64

Constructing DataFrame objects¶

From a single Series object¶

A DataFrame is a collection of Series objects, and a single-column DataFrame can be constructed from a single Series:

In [42]:
pd.DataFrame(population, columns=['population'])
Out[42]:
population
California 38332521
Texas 26448193
New York 19651127
Florida 19552860
Illinois 12882135

From a list of dicts¶

In [48]:
data = [{'a': i, 'b': 2 * i} for i in range(3)]
data
Out[48]:
[{'a': 0, 'b': 0}, {'a': 1, 'b': 2}, {'a': 2, 'b': 4}]
In [49]:
pd.DataFrame(data)
Out[49]:
a b
0 0 0
1 1 2
2 2 4
In [23]:
pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}]) #  Pandas will fill missing keys with ``NaN``
Out[23]:
a b c
0 1.0 2 NaN
1 NaN 3 4.0

From a two-dimensional NumPy array¶

Given a two-dimensional array of data, we can create a DataFrame with any specified column and index names:

In [50]:
np.random.rand(3, 2)
Out[50]:
array([[0.30282887, 0.48376433],
       [0.53588853, 0.97428136],
       [0.94756199, 0.46766408]])
In [51]:
pd.DataFrame(np.random.rand(3, 2),
             columns=['foo', 'bar'],
             index=['a', 'b', 'c'])
Out[51]:
foo bar
a 0.759907 0.458958
b 0.776779 0.767430
c 0.131552 0.740137

From a NumPy structured array¶

In [52]:
A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])
A
Out[52]:
array([(0, 0.), (0, 0.), (0, 0.)], dtype=[('A', '<i8'), ('B', '<f8')])
In [53]:
pd.DataFrame(A)
Out[53]:
A B
0 0 0.0
1 0 0.0
2 0 0.0

The Pandas Index Object¶

This Index object is an interesting structure in itself, and it can be thought of either as an immutable array or as an ordered set (technically a multi-set, as Index objects may contain repeated values).

In [55]:
ind = pd.Index([2, 3, 5, 7, 11])
ind
Out[55]:
Int64Index([2, 3, 5, 7, 11], dtype='int64')

Index as immutable array¶

The Index in many ways operates like an array.

In [56]:
ind[1]
Out[56]:
3
In [58]:
ind[::2]
Out[58]:
Int64Index([2, 5, 11], dtype='int64')

Index objects also have many of the attributes familiar from NumPy arrays:

In [59]:
ind.size, ind.shape, ind.ndim, ind.dtype,
Out[59]:
(5, (5,), 1, dtype('int64'))

One difference is that indices are immutable–that is, they cannot be modified via the normal means:

In [60]:
ind[1] = 0

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-60-906a9fa1424c> in <module>
----> 1 ind[1] = 0

~/Developer/py-venvs/sphinx-venv/lib/python3.9/site-packages/pandas/core/indexes/base.py in __setitem__(self, key, value)
   4275     @final
   4276     def __setitem__(self, key, value):
-> 4277         raise TypeError("Index does not support mutable operations")
   4278 
   4279     def __getitem__(self, key):

TypeError: Index does not support mutable operations

Index as ordered set¶

The Index object follows many of the conventions used by Python's built-in set data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:

In [61]:
indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])
In [62]:
indA.intersection(indB)  # intersection
Out[62]:
Int64Index([3, 5, 7], dtype='int64')
In [39]:
indA.union(indB)  # union
Out[39]:
Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')
In [40]:
indA.symmetric_difference(indB)  # symmetric difference
Out[40]:
Int64Index([1, 2, 9, 11], dtype='int64')

Data Indexing and Selection¶

To modify values in NumPy arrays we use indexing (e.g., arr[2, 1]), slicing (e.g., arr[:, 1:5]), masking (e.g., arr[arr > 0]), fancy indexing (e.g., arr[0, [1, 5]]), and combinations thereof (e.g., arr[:, [1, 5]]).

Here we'll look at similar means of accessing and modifying values in Pandas Series and DataFrame objects. If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.

Data Selection in Series¶

A Series object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.

Series as dictionary¶

Like a dictionary, the Series object provides a mapping from a collection of keys to a collection of values:

In [63]:
data = pd.Series([0.25, 0.5, 0.75, 1.0], index=['a', 'b', 'c', 'd'])
data
Out[63]:
a    0.25
b    0.50
c    0.75
d    1.00
dtype: float64
In [64]:
data['b'] # mnemonic indexing
Out[64]:
0.5
In [65]:
'a' in data # dictionary-like Python expressions...
Out[65]:
True
In [67]:
data.keys() # ...and methods.
Out[67]:
Index(['a', 'b', 'c', 'd'], dtype='object')
In [68]:
list(data.items())
Out[68]:
[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]

Series objects can even be modified with a dictionary-like syntax:

In [69]:
data['e'] = 1.25
data
Out[69]:
a    0.25
b    0.50
c    0.75
d    1.00
e    1.25
dtype: float64

This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place.

Series as one-dimensional array¶

A Series builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays – that is, slices, masking, and fancy indexing:

In [47]:
data['a':'c'] # slicing by explicit index
Out[47]:
a    0.25
b    0.50
c    0.75
dtype: float64
In [48]:
data[0:2] # slicing by implicit integer index
Out[48]:
a    0.25
b    0.50
dtype: float64
In [49]:
data[(data > 0.3) & (data < 0.8)] # masking
Out[49]:
b    0.50
c    0.75
dtype: float64

because

In [70]:
(data > 0.3) & (data < 0.8)
Out[70]:
a    False
b     True
c     True
d    False
e    False
dtype: bool
In [71]:
type(_)
Out[71]:
pandas.core.series.Series
In [50]:
data[['a', 'e']] # fancy indexing
Out[50]:
a    0.25
e    1.25
dtype: float64

Notice that when slicing with an explicit index (i.e., data['a':'c']), the final index is included in the slice, while when slicing with an implicit index (i.e., data[0:2]), the final index is excluded from the slice.

Indexers: loc, iloc, and ix¶

If your Series has an explicit integer index, an indexing operation such as data[1] will use the explicit indices, while a slicing operation like data[1:3] will use the implicit Python-style index.

In [72]:
data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])
data
Out[72]:
1    a
3    b
5    c
dtype: object
In [73]:
data[1] # explicit index when indexing
Out[73]:
'a'
In [74]:
data[1:3] # implicit index when slicing
Out[74]:
3    b
5    c
dtype: object

Because of this potential confusion in the case of integer indexes, Pandas provides some special indexer attributes that explicitly expose certain indexing schemes.

These are not functional methods, but attributes that expose a particular slicing interface to the data in the Series.

First, the loc attribute allows indexing and slicing that always references the explicit index:

In [75]:
data.loc[1]
Out[75]:
'a'
In [76]:
data.loc[1:3]
Out[76]:
1    a
3    b
dtype: object

The iloc attribute allows indexing and slicing that always references the implicit Python-style index:

In [77]:
data.iloc[1:3]
Out[77]:
3    b
5    c
dtype: object

A third indexing attribute, ix, is a hybrid of the two, and for Series objects is equivalent to standard []-based indexing.

The purpose of the ix indexer will become more apparent in the context of DataFrame objects.

Data Selection in DataFrame¶

Recall that a DataFrame acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of Series structures sharing the same index.

In [78]:
area = pd.Series({'California': 423967, 'Texas': 695662,
                  'New York': 141297, 'Florida': 170312,
                  'Illinois': 149995})
pop = pd.Series({'California': 38332521, 'Texas': 26448193,
                 'New York': 19651127, 'Florida': 19552860,
                 'Illinois': 12882135})
In [79]:
data = pd.DataFrame({'area':area, 'pop':pop})
data
Out[79]:
area pop
California 423967 38332521
Texas 695662 26448193
New York 141297 19651127
Florida 170312 19552860
Illinois 149995 12882135
In [80]:
data['area'] # columns can be accessed via dict-style indexing
Out[80]:
California    423967
Texas         695662
New York      141297
Florida       170312
Illinois      149995
Name: area, dtype: int64
In [81]:
data.area # alternatively, use attribute-style access with column names
Out[81]:
California    423967
Texas         695662
New York      141297
Florida       170312
Illinois      149995
Name: area, dtype: int64

this dictionary-style syntax can also be used to modify the object, in this case adding a new column:

In [82]:
data['density'] = data['pop'] / data['area']
data
Out[82]:
area pop density
California 423967 38332521 90.413926
Texas 695662 26448193 38.018740
New York 141297 19651127 139.076746
Florida 170312 19552860 114.806121
Illinois 149995 12882135 85.883763

DataFrame as two-dimensional array¶

DataFrame can also be viewed as an enhanced two-dimensional array:

In [83]:
data.values # examine the raw underlying data array
Out[83]:
array([[4.23967000e+05, 3.83325210e+07, 9.04139261e+01],
       [6.95662000e+05, 2.64481930e+07, 3.80187404e+01],
       [1.41297000e+05, 1.96511270e+07, 1.39076746e+02],
       [1.70312000e+05, 1.95528600e+07, 1.14806121e+02],
       [1.49995000e+05, 1.28821350e+07, 8.58837628e+01]])
In [87]:
data.values.T
Out[87]:
array([[4.23967000e+05, 6.95662000e+05, 1.41297000e+05, 1.70312000e+05,
        1.49995000e+05],
       [3.83325210e+07, 2.64481930e+07, 1.96511270e+07, 1.95528600e+07,
        1.28821350e+07],
       [9.04139261e+01, 3.80187404e+01, 1.39076746e+02, 1.14806121e+02,
        8.58837628e+01]])
In [88]:
type(_)
Out[88]:
numpy.ndarray
In [89]:
data.T # transpose the full DataFrame object
Out[89]:
California Texas New York Florida Illinois
area 4.239670e+05 6.956620e+05 1.412970e+05 1.703120e+05 1.499950e+05
pop 3.833252e+07 2.644819e+07 1.965113e+07 1.955286e+07 1.288214e+07
density 9.041393e+01 3.801874e+01 1.390767e+02 1.148061e+02 8.588376e+01
In [90]:
type(_)
Out[90]:
pandas.core.frame.DataFrame
In [91]:
data.values[0] # passing a single index to an array accesses a row
Out[91]:
array([4.23967000e+05, 3.83325210e+07, 9.04139261e+01])
In [92]:
data['area'] # assing a single "index" to access a column
Out[92]:
California    423967
Texas         695662
New York      141297
Florida       170312
Illinois      149995
Name: area, dtype: int64

Using the iloc indexer, we can index the underlying array as if it is a simple NumPy array (using the implicit Python-style index)

In [93]:
data.iloc[:3, :2]
Out[93]:
area pop
California 423967 38332521
Texas 695662 26448193
New York 141297 19651127

Similarly, using the loc indexer we can index the underlying data in an array-like style but using the explicit index and column names:

In [67]:
data.loc[:'Illinois', :'pop']
Out[67]:
area pop
California 423967 38332521
Texas 695662 26448193
New York 141297 19651127
Florida 170312 19552860
Illinois 149995 12882135

Any of the familiar NumPy-style data access patterns can be used within these indexers.

In [68]:
data.loc[data.density > 100, ['pop', 'density']]
Out[68]:
pop density
New York 19651127 139.076746
Florida 19552860 114.806121

Any of these indexing conventions may also be used to set or modify values; this is done in the standard way that you might be accustomed to from working with NumPy:

In [69]:
data.iloc[0, 2] = 90
data
Out[69]:
area pop density
California 423967 38332521 90.000000
Texas 695662 26448193 38.018740
New York 141297 19651127 139.076746
Florida 170312 19552860 114.806121
Illinois 149995 12882135 85.883763

Additional indexing conventions¶

In [70]:
data['Florida':'Illinois'] # *slicing* refers to rows
Out[70]:
area pop density
Florida 170312 19552860 114.806121
Illinois 149995 12882135 85.883763
In [71]:
data[data.density > 100] # direct masking operations are also interpreted row-wise
Out[71]:
area pop density
New York 141297 19651127 139.076746
Florida 170312 19552860 114.806121

Operating on Data in Pandas¶

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.).

Pandas inherits much of this functionality from NumPy.

Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will preserve index and column labels in the output, and for binary operations such as addition and multiplication, Pandas will automatically align indices when passing the objects to the ufunc.

Ufuncs: Index Preservation¶

Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas Series and DataFrame objects:

In [94]:
rng = np.random.RandomState(42)
ser = pd.Series(rng.randint(0, 10, 4))
ser
Out[94]:
0    6
1    3
2    7
3    4
dtype: int64
In [96]:
rng.randint(0, 10, (3, 4))
Out[96]:
array([[1, 7, 5, 1],
       [4, 0, 9, 5],
       [8, 0, 9, 2]])
In [97]:
df = pd.DataFrame(rng.randint(0, 10, (3, 4)), columns=['A', 'B', 'C', 'D'])
df
Out[97]:
A B C D
0 6 3 8 2
1 4 2 6 4
2 8 6 1 3

If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object with the indices preserved:

In [100]:
np.exp(ser)
Out[100]:
0     403.428793
1      20.085537
2    1096.633158
3      54.598150
dtype: float64
In [101]:
type(_)
Out[101]:
pandas.core.series.Series
In [102]:
np.sin(df * np.pi / 4) # a slightly more complex calculation
Out[102]:
A B C D
0 -1.000000e+00 0.707107 -2.449294e-16 1.000000e+00
1 1.224647e-16 1.000000 -1.000000e+00 1.224647e-16
2 -2.449294e-16 -1.000000 7.071068e-01 7.071068e-01
In [103]:
type(_)
Out[103]:
pandas.core.frame.DataFrame

UFuncs: Index Alignment¶

For binary operations on two Series or DataFrame objects, Pandas will align indices in the process of performing the operation.

Index alignment in Series¶

Suppose we are combining two different data sources, and find only the top three US states by area and the top three US states by population:

In [104]:
area = pd.Series({'Alaska': 1723337, 'Texas': 695662, 'California': 423967}, name='area')
population = pd.Series({'California': 38332521, 'Texas': 26448193, 'New York': 19651127}, name='population')
In [106]:
population / area
Out[106]:
Alaska              NaN
California    90.413926
New York            NaN
Texas         38.018740
dtype: float64

The resulting array contains the union of indices of the two input arrays, which could be determined using standard Python set arithmetic on these indices:

In [107]:
area.index.union(population.index) # this does create a new index and doesn't modify in place.
Out[107]:
Index(['Alaska', 'California', 'New York', 'Texas'], dtype='object')
In [108]:
area.index
Out[108]:
Index(['Alaska', 'Texas', 'California'], dtype='object')

Any item for which one or the other does not have an entry is marked with NaN, or "Not a Number," which is how Pandas marks missing data . This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are filled in with NaN by default:

In [109]:
A = pd.Series([2, 4, 6], index=[0, 1, 2])
B = pd.Series([1, 3, 5], index=[1, 2, 3])
A + B
Out[109]:
0    NaN
1    5.0
2    9.0
3    NaN
dtype: float64

If using NaN values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators:

In [110]:
A.add(B, fill_value=0)
Out[110]:
0    2.0
1    5.0
2    9.0
3    5.0
dtype: float64

Index alignment in DataFrame¶

A similar type of alignment takes place for both columns and indices when performing operations on DataFrames:

In [111]:
A = pd.DataFrame(rng.randint(0, 20, (2, 2)), columns=list('AB'))
A
Out[111]:
A B
0 13 17
1 8 1
In [113]:
B = pd.DataFrame(rng.randint(0, 10, (3, 3)), columns=list('BAC'))
B
Out[113]:
B A C
0 1 5 5
1 9 3 5
2 1 9 1
In [114]:
A + B
Out[114]:
A B C
0 18.0 18.0 NaN
1 11.0 10.0 NaN
2 NaN NaN NaN
In [116]:
fill = A.stack().mean()
fill
Out[116]:
9.75
In [117]:
A.add(B, fill_value=fill)
Out[117]:
A B C
0 18.00 18.00 14.75
1 11.00 10.00 14.75
2 18.75 10.75 10.75

The following table lists Python operators and their equivalent Pandas object methods:

Python Operator Pandas Method(s)
+ add()
- sub(), subtract()
* mul(), multiply()
/ truediv(), div(), divide()
// floordiv()
% mod()
** pow()

Ufuncs: Operations Between DataFrame and Series¶

When performing operations between a DataFrame and a Series, the index and column alignment is similarly maintained. Operations between a DataFrame and a Series are similar to operations between a two-dimensional and one-dimensional NumPy array.

In [87]:
A = rng.randint(10, size=(3, 4))
A
Out[87]:
array([[3, 8, 2, 4],
       [2, 6, 4, 8],
       [6, 1, 3, 8]])
In [89]:
type(A)
Out[89]:
numpy.ndarray
In [88]:
A - A[0]
Out[88]:
array([[ 0,  0,  0,  0],
       [-1, -2,  2,  4],
       [ 3, -7,  1,  4]])

According to NumPy's broadcasting rules , subtraction between a two-dimensional array and one of its rows is applied row-wise.

In Pandas, the convention similarly operates row-wise by default:

In [90]:
df = pd.DataFrame(A, columns=list('QRST'))
df - df.iloc[0]
Out[90]:
Q R S T
0 0 0 0 0
1 -1 -2 2 4
2 3 -7 1 4

If you would instead like to operate column-wise you have to specify the axis keyword:

In [91]:
df.subtract(df['R'], axis=0)
Out[91]:
Q R S T
0 -5 0 -6 -4
1 -4 0 -2 2
2 5 0 2 7

Handling Missing Data¶

The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous. In particular, many interesting datasets will have some amount of data missing.

To make matters even more complicated, different data sources may indicate missing data in different ways.

Trade-Offs in Missing Data Conventions¶

To indicate the presence of missing data in a table or DataFrame we can use two strategies: using a mask that globally indicates missing values, or choosing a sentinel value that indicates a missing entry.

In the masking approach, the mask might be an entirely separate Boolean array, or it may involve appropriation of one bit in the data representation to locally indicate the null status of a value.

In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with -9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with NaN (Not a Number).

None of these approaches is without trade-offs: use of a separate mask array requires allocation of an additional Boolean array. A sentinel value reduces the range of valid values that can be represented, and may require extra (often non-optimized) logic in CPU and GPU arithmetic.

Missing Data in Pandas¶

The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types.

NumPy does have support for masked arrays – that is, arrays that have a separate Boolean mask array attached for marking data as "good" or "bad." Pandas could have derived from this, but the overhead in both storage, computation, and code maintenance makes that an unattractive choice.

With these constraints in mind, Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point NaN value, and the Python None object.

None: Pythonic missing data¶

The first sentinel value used by Pandas is None, a Python singleton object that is often used for missing data in Python code.

Because it is a Python object, None cannot be used in any arbitrary NumPy/Pandas array, but only in arrays with data type 'object' (i.e., arrays of Python objects):

In [118]:
vals1 = np.array([1, None, 3, 4])
vals1
Out[118]:
array([1, None, 3, 4], dtype=object)

Any operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:

In [93]:
for dtype in ['object', 'int']:
    print("dtype =", dtype)
    %timeit np.arange(1E6, dtype=dtype).sum()
    print()

dtype = object
81.8 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

dtype = int
1.87 ms ± 34.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

The use of Python objects in an array also means that if you perform aggregations like sum() or min() across an array with a None value, you will generally get an error:

In [94]:
vals1.sum()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-94-30a3fc8c6726> in <module>
----> 1 vals1.sum()

~/Developer/venvs/py-ml/lib/python3.8/site-packages/numpy/core/_methods.py in _sum(a, axis, dtype, out, keepdims, initial, where)
     45 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
     46          initial=_NoValue, where=True):
---> 47     return umr_sum(a, axis, dtype, out, keepdims, initial, where)
     48 
     49 def _prod(a, axis=None, dtype=None, out=None, keepdims=False,

TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

NaN: Missing numerical data¶

The other missing data representation, NaN (acronym for Not a Number), is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:

In [119]:
vals2 = np.array([1, np.nan, 3, 4]) 
vals2.dtype
Out[119]:
dtype('float64')
In [120]:
1 + np.nan, 0 *  np.nan
Out[120]:
(nan, nan)
In [121]:
vals2.sum(), vals2.min(), vals2.max()
Out[121]:
(nan, nan, nan)

NumPy does provide some special aggregations that will ignore these missing values:

In [98]:
np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2)
Out[98]:
(8.0, 1.0, 4.0)

NaN and None in Pandas¶

NaN and None both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:

In [99]:
pd.Series([1, np.nan, 2, None])
Out[99]:
0    1.0
1    NaN
2    2.0
3    NaN
dtype: float64

The following table lists the upcasting conventions in Pandas when NA values are introduced:

Typeclass Conversion When Storing NAs NA Sentinel Value
floating No change np.nan
object No change None or np.nan
integer Cast to float64 np.nan
boolean Cast to object None or np.nan

Keep in mind that in Pandas, string data is always stored with an object dtype.

Operating on Null Values¶

As we have seen, Pandas treats None and NaN as essentially interchangeable for indicating missing or null values. To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures. They are:

  • isnull(): Generate a boolean mask indicating missing values
  • notnull(): Opposite of isnull()
  • dropna(): Return a filtered version of the data
  • fillna(): Return a copy of the data with missing values filled or imputed

Detecting null values¶

Pandas data structures have two useful methods for detecting null data: isnull() and notnull(). Either one will return a Boolean mask over the data:

In [122]:
data = pd.Series([1, np.nan, 'hello', None])
data.isnull()
Out[122]:
0    False
1     True
2    False
3     True
dtype: bool

Dropping null values¶

In addition to the masking used before, there are the convenience methods, dropna() (which removes NA values) and fillna() (which fills in NA values):

In [101]:
data.dropna()
Out[101]:
0        1
2    hello
dtype: object

For a DataFrame, there are more options:

In [123]:
df = pd.DataFrame([[1,      np.nan, 2],
                   [2,      3,      5],
                   [np.nan, 4,      6]])
df
Out[123]:
0 1 2
0 1.0 NaN 2
1 2.0 3.0 5
2 NaN 4.0 6
In [124]:
df.dropna() # drop all rows in which *any* null value is present
Out[124]:
0 1 2
1 2.0 3.0 5
In [104]:
df.dropna(axis='columns') # drop NA values from all columns containing a null value
Out[104]:
2
0 2
1 5
2 6

The default is how='any', such that any row or column (depending on the axis keyword) containing a null value will be dropped.

In [105]:
df[3] = np.nan
df
Out[105]:
0 1 2 3
0 1.0 NaN 2 NaN
1 2.0 3.0 5 NaN
2 NaN 4.0 6 NaN

You can also specify how='all', which will only drop rows/columns that are all null values:

In [106]:
df.dropna(axis='columns', how='all')
Out[106]:
0 1 2
0 1.0 NaN 2
1 2.0 3.0 5
2 NaN 4.0 6

The thresh parameter lets you specify a minimum number of non-null values for the row/column to be kept:

In [107]:
df.dropna(axis='rows', thresh=3)
Out[107]:
0 1 2 3
1 2.0 3.0 5 NaN

Filling null values¶

Sometimes rather than dropping NA values, you'd rather replace them with a valid value. This value might be a single number like zero, or it might be some sort of imputation or interpolation from the good values. You could do this in-place using the isnull() method as a mask, but because it is such a common operation Pandas provides the fillna() method, which returns a copy of the array with the null values replaced.

In [108]:
data = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))
data
Out[108]:
a    1.0
b    NaN
c    2.0
d    NaN
e    3.0
dtype: float64
In [109]:
data.fillna(0) # fill NA entries with a single value
Out[109]:
a    1.0
b    0.0
c    2.0
d    0.0
e    3.0
dtype: float64
In [110]:
data.fillna(method='ffill') # specify a forward-fill to propagate the previous value forward
Out[110]:
a    1.0
b    1.0
c    2.0
d    2.0
e    3.0
dtype: float64
In [111]:
data.fillna(method='bfill') # specify a back-fill to propagate the next values backward
Out[111]:
a    1.0
b    2.0
c    2.0
d    3.0
e    3.0
dtype: float64

For DataFrames, the options are similar, but we can also specify an axis along which the fills take place:

In [112]:
df
Out[112]:
0 1 2 3
0 1.0 NaN 2 NaN
1 2.0 3.0 5 NaN
2 NaN 4.0 6 NaN
In [113]:
df.fillna(method='ffill', axis=1)
Out[113]:
0 1 2 3
0 1.0 1.0 2.0 2.0
1 2.0 3.0 5.0 5.0
2 NaN 4.0 6.0 6.0

Hierarchical Indexing¶

Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas Series and DataFrame objects, respectively. Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys.

A far more common pattern in practice is to make use of hierarchical indexing (also known as multi-indexing) to incorporate multiple index levels within a single index. In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional Series and two-dimensional DataFrame objects.

A Multiply Indexed Series¶

Let's start by considering how we might represent two-dimensional data within a one-dimensional Series.

The bad way¶

Suppose you would like to track data about states from two different years. Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys:

In [125]:
index = [('California', 2000), ('California', 2010),
         ('New York', 2000), ('New York', 2010),
         ('Texas', 2000), ('Texas', 2010)]
populations = [33871648, 37253956,
               18976457, 19378102,
               20851820, 25145561]
pop = pd.Series(populations, index=index)
pop
Out[125]:
(California, 2000)    33871648
(California, 2010)    37253956
(New York, 2000)      18976457
(New York, 2010)      19378102
(Texas, 2000)         20851820
(Texas, 2010)         25145561
dtype: int64

If you need to select all values from 2010, you'll need to do some messy (and potentially slow) munging to make it happen:

In [115]:
pop[[i for i in pop.index if i[1] == 2010]]
Out[115]:
(California, 2010)    37253956
(New York, 2010)      19378102
(Texas, 2010)         25145561
dtype: int64

The Better Way: Pandas MultiIndex¶

Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas MultiIndex type gives us the type of operations we wish to have:

In [126]:
index = pd.MultiIndex.from_tuples(index)
index
Out[126]:
MultiIndex([('California', 2000),
            ('California', 2010),
            (  'New York', 2000),
            (  'New York', 2010),
            (     'Texas', 2000),
            (     'Texas', 2010)],
           )
In [127]:
type(_)
Out[127]:
pandas.core.indexes.multi.MultiIndex

A MultiIndex contains multiple levels of indexing–in this case, the state names and the years, as well as multiple labels for each data point which encode these levels.

If we re-index our series with this MultiIndex, we see the hierarchical representation of the data:

In [128]:
pop = pop.reindex(index)
pop
Out[128]:
California  2000    33871648
            2010    37253956
New York    2000    18976457
            2010    19378102
Texas       2000    20851820
            2010    25145561
dtype: int64

Here the first two columns of the Series representation show the multiple index values, while the third column shows the data.

Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it.

Now to access all data for which the second index is 2010, we can simply use the Pandas slicing notation:

In [118]:
pop[:, 2010]
Out[118]:
California    37253956
New York      19378102
Texas         25145561
dtype: int64

The result is a singly indexed array with just the keys we're interested in. This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with.

MultiIndex as extra dimension¶

We could have stored the same data using a simple DataFrame with index and column labels; in fact, Pandas is built with this equivalence in mind.

The unstack() method will quickly convert a multiply indexed Series into a conventionally indexed DataFrame:

In [129]:
pop_df = pop.unstack()
pop_df
Out[129]:
2000 2010
California 33871648 37253956
New York 18976457 19378102
Texas 20851820 25145561
In [131]:
type(pop_df)
Out[131]:
pandas.core.frame.DataFrame

Naturally, the stack() method provides the opposite operation:

In [130]:
pop_df.stack()
Out[130]:
California  2000    33871648
            2010    37253956
New York    2000    18976457
            2010    19378102
Texas       2000    20851820
            2010    25145561
dtype: int64

Seeing this, you might wonder why would we would bother with hierarchical indexing at all.

The reason is simple: just as we were able to use multi-indexing to represent two-dimensional data within a one-dimensional Series, we can also use it to represent data of three or more dimensions in a Series or DataFrame.

Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent.

Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18) ; with a MultiIndex this is as easy as adding another column to the DataFrame:

In [132]:
pop_df = pd.DataFrame({'total': pop,
                       'under18': [9267089, 9284094,
                                   4687374, 4318033,
                                   5906301, 6879014]})
pop_df
Out[132]:
total under18
California 2000 33871648 9267089
2010 37253956 9284094
New York 2000 18976457 4687374
2010 19378102 4318033
Texas 2000 20851820 5906301
2010 25145561 6879014

In addition, all the ufuncs work with hierarchical indices as well:

In [125]:
f_u18 = pop_df['under18'] / pop_df['total']
f_u18.unstack()
Out[125]:
2000 2010
California 0.273594 0.249211
New York 0.247010 0.222831
Texas 0.283251 0.273568

Methods of MultiIndex Creation¶

The most straightforward way to construct a multiply indexed Series or DataFrame is to simply pass a list of two or more index arrays to the constructor:

In [126]:
df = pd.DataFrame(np.random.rand(4, 2),
                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
                  columns=['data1', 'data2'])
df
Out[126]:
data1 data2
a 1 0.482545 0.352967
2 0.574280 0.063582
b 1 0.102271 0.569372
2 0.753026 0.194597

Similarly, if you pass a dictionary with appropriate tuples as keys, Pandas will automatically recognize this and use a MultiIndex by default:

In [127]:
data = {('California', 2000): 33871648,
        ('California', 2010): 37253956,
        ('Texas', 2000): 20851820,
        ('Texas', 2010): 25145561,
        ('New York', 2000): 18976457,
        ('New York', 2010): 19378102}
pd.Series(data)
Out[127]:
California  2000    33871648
            2010    37253956
Texas       2000    20851820
            2010    25145561
New York    2000    18976457
            2010    19378102
dtype: int64

Explicit MultiIndex constructors¶

For more flexibility in how the index is constructed, you can instead use the class method constructors available in the pd.MultiIndex.

You can construct the MultiIndex from a simple list of arrays giving the index values within each level:

In [128]:
pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])
Out[128]:
MultiIndex([('a', 1),
            ('a', 2),
            ('b', 1),
            ('b', 2)],
           )

You can even construct it from a Cartesian product of single indices:

In [129]:
pd.MultiIndex.from_product([['a', 'b'], [1, 2]])
Out[129]:
MultiIndex([('a', 1),
            ('a', 2),
            ('b', 1),
            ('b', 2)],
           )

MultiIndex level names¶

Sometimes it is convenient to name the levels of the MultiIndex. This can be accomplished by passing the names argument to any of the above MultiIndex constructors, or by setting the names attribute of the index after the fact:

In [130]:
pop.index.names = ['state', 'year']
pop
Out[130]:
state       year
California  2000    33871648
            2010    37253956
New York    2000    18976457
            2010    19378102
Texas       2000    20851820
            2010    25145561
dtype: int64

MultiIndex for columns¶

In a DataFrame, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well:

In [132]:
index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]], names=['year', 'visit'])
columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']], names=['subject', 'type'])

data = np.round(np.random.randn(4, 6), 1) # mock some data
data[:, ::2] *= 10
data += 37

health_data = pd.DataFrame(data, index=index, columns=columns)
health_data # create the DataFrame
Out[132]:
subject Bob Guido Sue
type HR Temp HR Temp HR Temp
year visit
2013 1 48.0 38.1 19.0 38.4 52.0 38.8
2 34.0 38.0 37.0 36.9 31.0 37.6
2014 1 41.0 37.0 52.0 38.9 38.0 37.4
2 47.0 36.9 46.0 36.4 42.0 36.6

This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number; we can index the top-level column by the person's name and get a full DataFrame containing just that person's information:

In [133]:
health_data['Guido']
Out[133]:
type HR Temp
year visit
2013 1 19.0 38.4
2 37.0 36.9
2014 1 52.0 38.9
2 46.0 36.4

Indexing and Slicing a MultiIndex¶

Indexing and slicing on a MultiIndex is designed to be intuitive, and it helps if you think about the indices as added dimensions.

Multiply indexed Series¶

Consider the multiply indexed Series of state populations we saw earlier:

In [134]:
pop
Out[134]:
state       year
California  2000    33871648
            2010    37253956
New York    2000    18976457
            2010    19378102
Texas       2000    20851820
            2010    25145561
dtype: int64
In [135]:
pop['California', 2000] # access single elements by indexing with multiple terms
Out[135]:
33871648

The MultiIndex also supports partial indexing, or indexing just one of the levels in the index. The result is another Series, with the lower-level indices maintained:

In [136]:
pop['California']
Out[136]:
year
2000    33871648
2010    37253956
dtype: int64

Other types of indexing and selection could be based either on Boolean masks:

In [137]:
pop[pop > 22000000]
Out[137]:
state       year
California  2000    33871648
            2010    37253956
Texas       2010    25145561
dtype: int64

or on fancy indexing:

In [138]:
pop[['California', 'Texas']]
Out[138]:
state       year
California  2000    33871648
            2010    37253956
Texas       2000    20851820
            2010    25145561
dtype: int64

Multiply indexed DataFrames¶

A multiply indexed DataFrame behaves in a similar manner:

In [139]:
health_data
Out[139]:
subject Bob Guido Sue
type HR Temp HR Temp HR Temp
year visit
2013 1 48.0 38.1 19.0 38.4 52.0 38.8
2 34.0 38.0 37.0 36.9 31.0 37.6
2014 1 41.0 37.0 52.0 38.9 38.0 37.4
2 47.0 36.9 46.0 36.4 42.0 36.6

Remember that columns are primary in a DataFrame, and the syntax used for multiply indexed Series applies to the columns.

We can recover Guido's heart rate data with a simple operation:

In [140]:
health_data['Guido', 'HR']
Out[140]:
year  visit
2013  1        19.0
      2        37.0
2014  1        52.0
      2        46.0
Name: (Guido, HR), dtype: float64

Also, as with the single-index case, we can use the loc, iloc, and ix indexers:

In [141]:
health_data.iloc[:2, :2]
Out[141]:
subject Bob
type HR Temp
year visit
2013 1 48.0 38.1
2 34.0 38.0

These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in loc or iloc can be passed a tuple of multiple indices:

In [142]:
health_data.loc[:, ('Bob', 'HR')]
Out[142]:
year  visit
2013  1        48.0
      2        34.0
2014  1        41.0
      2        47.0
Name: (Bob, HR), dtype: float64

Rearranging Multi-Indices¶

One of the keys to working with multiply indexed data is knowing how to effectively transform the data.

There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations.

We saw a brief example of this in the stack() and unstack() methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns.

Sorted and unsorted indices¶

Earlier, we briefly mentioned a caveat, but we should emphasize it more here.

Many of the MultiIndex slicing operations will fail if the index is not sorted.

We'll start by creating some simple multiply indexed data where the indices are not lexographically sorted:

In [143]:
index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])
data = pd.Series(np.random.rand(6), index=index)
data.index.names = ['char', 'int']
data
Out[143]:
char  int
a     1      0.002105
      2      0.280923
c     1      0.008604
      2      0.631968
b     1      0.072270
      2      0.273800
dtype: float64
In [144]:
try:
    data['a':'b'] # try to take a partial slice of this index
except KeyError as e:
    print(type(e))
    print(e)

<class 'pandas.errors.UnsortedIndexError'>
'Key length (1) was greater than MultiIndex lexsort depth (0)'

This is the result of the MultiIndex not being sorted; in general, partial slices and other similar operations require the levels in the MultiIndex to be in sorted (i.e., lexographical) order.

Pandas provides a number of convenience routines to perform this type of sorting; examples are the sort_index() and sortlevel() methods of the DataFrame.

In [145]:
data = data.sort_index()
data
Out[145]:
char  int
a     1      0.002105
      2      0.280923
b     1      0.072270
      2      0.273800
c     1      0.008604
      2      0.631968
dtype: float64

With the index sorted in this way, partial slicing will work as expected:

In [146]:
data['a':'b']
Out[146]:
char  int
a     1      0.002105
      2      0.280923
b     1      0.072270
      2      0.273800
dtype: float64

Stacking and unstacking indices¶

As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use:

In [147]:
pop.unstack(level=0)
Out[147]:
state California New York Texas
year
2000 33871648 18976457 20851820
2010 37253956 19378102 25145561
In [148]:
pop.unstack(level=1)
Out[148]:
year 2000 2010
state
California 33871648 37253956
New York 18976457 19378102
Texas 20851820 25145561

The opposite of unstack() is stack(), which here can be used to recover the original series:

In [149]:
pop.unstack().stack()
Out[149]:
state       year
California  2000    33871648
            2010    37253956
New York    2000    18976457
            2010    19378102
Texas       2000    20851820
            2010    25145561
dtype: int64

Index setting and resetting¶

Another way to rearrange hierarchical data is to turn the index labels into columns; this can be accomplished with the reset_index method.

Calling this on the population dictionary will result in a DataFrame with a state and year column holding the information that was formerly in the index.

In [150]:
pop_flat = pop.reset_index(name='population') # specify the name of the data for the column
pop_flat
Out[150]:
state year population
0 California 2000 33871648
1 California 2010 37253956
2 New York 2000 18976457
3 New York 2010 19378102
4 Texas 2000 20851820
5 Texas 2010 25145561

Often when working with data in the real world, the raw input data looks like this and it's useful to build a MultiIndex from the column values. This can be done with the set_index method of the DataFrame, which returns a multiply indexed DataFrame:

In [151]:
pop_flat.set_index(['state', 'year'])
Out[151]:
population
state year
California 2000 33871648
2010 37253956
New York 2000 18976457
2010 19378102
Texas 2000 20851820
2010 25145561

Data Aggregations on Multi-Indices¶

We've previously seen that Pandas has built-in data aggregation methods, such as mean(), sum(), and max(). For hierarchically indexed data, these can be passed a level parameter that controls which subset of the data the aggregate is computed on.

In [152]:
health_data
Out[152]:
subject Bob Guido Sue
type HR Temp HR Temp HR Temp
year visit
2013 1 48.0 38.1 19.0 38.4 52.0 38.8
2 34.0 38.0 37.0 36.9 31.0 37.6
2014 1 41.0 37.0 52.0 38.9 38.0 37.4
2 47.0 36.9 46.0 36.4 42.0 36.6

Perhaps we'd like to average-out the measurements in the two visits each year. We can do this by naming the index level we'd like to explore, in this case the year:

In [153]:
data_mean = health_data.mean(level='year')
data_mean
Out[153]:
subject Bob Guido Sue
type HR Temp HR Temp HR Temp
year
2013 41.0 38.05 28.0 37.65 41.5 38.2
2014 44.0 36.95 49.0 37.65 40.0 37.0

By further making use of the axis keyword, we can take the mean among levels on the columns as well:

In [154]:
data_mean.mean(axis=1, level='type')
Out[154]:
type HR Temp
year
2013 36.833333 37.966667
2014 44.333333 37.200000

Combining Datasets: Concat and Append¶

Some of the most interesting studies of data come from combining different data sources. These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets. Series and DataFrames are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.

Here we'll take a look at simple concatenation of Series and DataFrames with the pd.concat function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.

For convenience, we'll define this function which creates a DataFrame of a particular form that will be useful below:

In [176]:
def make_df(cols, ind):
    """Quickly make a DataFrame"""
    data = {c: [str(c) + str(i) for i in ind]
            for c in cols}
    return pd.DataFrame(data, ind)

# example DataFrame
make_df('ABC', range(3))
Out[176]:
A B C
0 A0 B0 C0
1 A1 B1 C1
2 A2 B2 C2

In addition, we'll create a quick class that allows us to display multiple DataFrames side by side. The code makes use of the special _repr_html_ method, which IPython uses to implement its rich object display:

In [178]:
class display(object):
    """Display HTML representation of multiple objects"""
    template = """<div style="float: left; padding: 10px;">
    <p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
    </div>"""
    def __init__(self, *args):
        self.args = args
        
    def _repr_html_(self):
        return '\n'.join(self.template.format(a, eval(a)._repr_html_())
                         for a in self.args)
    
    def __repr__(self):
        return '\n\n'.join(a + '\n' + repr(eval(a))
                           for a in self.args)

Simple Concatenation with pd.concat¶

Pandas has a function, pd.concat(), which has a similar syntax to np.concatenate but contains a number of options that we'll discuss momentarily:

# Signature in Pandas v0.18
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
          keys=None, levels=None, names=None, verify_integrity=False,
          copy=True)

pd.concat() can be used for a simple concatenation of Series or DataFrame objects, just as np.concatenate() can be used for simple concatenations of arrays:

In [180]:
ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3])
ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6])
pd.concat([ser1, ser2])
Out[180]:
1    A
2    B
3    C
4    D
5    E
6    F
dtype: object
In [181]:
df1 = make_df('AB', [1, 2])
df2 = make_df('AB', [3, 4])
display('df1', 'df2', 'pd.concat([df1, df2])')
Out[181]:

df1

A B
1 A1 B1
2 A2 B2

df2

A B
3 A3 B3
4 A4 B4

pd.concat([df1, df2])

A B
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4

By default, the concatenation takes place row-wise within the DataFrame (i.e., axis=0). Like np.concatenate, pd.concat allows specification of an axis along which concatenation will take place:

In [183]:
df3 = make_df('AB', [0, 1])
df4 = make_df('CD', [0, 1])
display('df3', 'df4', "pd.concat([df3, df4], axis=1)")
Out[183]:

df3

A B
0 A0 B0
1 A1 B1

df4

C D
0 C0 D0
1 C1 D1

pd.concat([df3, df4], axis=1)

A B C D
0 A0 B0 C0 D0
1 A1 B1 C1 D1

Duplicate indices¶

One important difference between np.concatenate and pd.concat is that Pandas concatenation preserves indices, even if the result will have duplicate indices:

In [184]:
x = make_df('AB', [0, 1])
y = make_df('AB', [2, 3])
y.index = x.index  # make duplicate indices!
display('x', 'y', 'pd.concat([x, y])')
Out[184]:

x

A B
0 A0 B0
1 A1 B1

y

A B
0 A2 B2
1 A3 B3

pd.concat([x, y])

A B
0 A0 B0
1 A1 B1
0 A2 B2
1 A3 B3

Notice the repeated indices in the result. While this is valid within DataFrames, the outcome is often undesirable. pd.concat() gives us a few ways to handle it.

In [185]:
try:
    pd.concat([x, y], verify_integrity=True)
except ValueError as e:
    print("ValueError:", e)

ValueError: Indexes have overlapping values: Int64Index([0, 1], dtype='int64')

Ignoring the index¶

Sometimes the index itself does not matter, and you would prefer it to simply be ignored. This option can be specified using the ignore_index flag. With this set to true, the concatenation will create a new integer index for the resulting Series:

In [186]:
display('x', 'y', 'pd.concat([x, y], ignore_index=True)')
Out[186]:

x

A B
0 A0 B0
1 A1 B1

y

A B
0 A2 B2
1 A3 B3

pd.concat([x, y], ignore_index=True)

A B
0 A0 B0
1 A1 B1
2 A2 B2
3 A3 B3

Adding MultiIndex keys¶

Another option is to use the keys option to specify a label for the data sources; the result will be a hierarchically indexed series containing the data:

In [187]:
display('x', 'y', "pd.concat([x, y], keys=['x', 'y'])")
Out[187]:

x

A B
0 A0 B0
1 A1 B1

y

A B
0 A2 B2
1 A3 B3

pd.concat([x, y], keys=['x', 'y'])

A B
x 0 A0 B0
1 A1 B1
y 0 A2 B2
1 A3 B3

Concatenation with joins¶

In practice, data from different sources might have different sets of column names, and pd.concat offers several options in this case. Consider the concatenation of the following two DataFrames, which have some (but not all!) columns in common:

In [188]:
df5 = make_df('ABC', [1, 2])
df6 = make_df('BCD', [3, 4])
display('df5', 'df6', 'pd.concat([df5, df6])')
Out[188]:

df5

A B C
1 A1 B1 C1
2 A2 B2 C2

df6

B C D
3 B3 C3 D3
4 B4 C4 D4

pd.concat([df5, df6])

A B C D
1 A1 B1 C1 NaN
2 A2 B2 C2 NaN
3 NaN B3 C3 D3
4 NaN B4 C4 D4

By default, the join is a union of the input column|s (join='outer'), but we can change this to an intersection of the columns using join='inner':

Another option is to directly specify the index of the remaininig colums using the join_axes argument, which takes a list of index objects.

In [190]:
display('df5', 'df6', "pd.concat([df5, df6])")
Out[190]:

df5

A B C
1 A1 B1 C1
2 A2 B2 C2

df6

B C D
3 B3 C3 D3
4 B4 C4 D4

pd.concat([df5, df6])

A B C D
1 A1 B1 C1 NaN
2 A2 B2 C2 NaN
3 NaN B3 C3 D3
4 NaN B4 C4 D4

The append() method¶

Because direct array concatenation is so common, Series and DataFrame objects have an append method that can accomplish the same thing in fewer keystrokes. For example, rather than calling pd.concat([df1, df2]), you can simply call df1.append(df2):

In [192]:
display('df1', 'df2', 'df1.append(df2)')
Out[192]:

df1

A B
1 A1 B1
2 A2 B2

df2

A B
3 A3 B3
4 A4 B4

df1.append(df2)

A B
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4

Keep in mind that unlike the append() and extend() methods of Python lists, the append() method in Pandas does not modify the original object–instead it creates a new object with the combined data.

It also is not a very efficient method, because it involves creation of a new index and data buffer. Thus, if you plan to do multiple append operations, it is generally better to build a list of DataFrames and pass them all at once to the concat() function.

Combining Datasets: Merge and Join¶

One essential feature offered by Pandas is its high-performance, in-memory join and merge operations. If you have ever worked with databases, you should be familiar with this type of data interaction. The main interface for this is the pd.merge function, and we'll see few examples of how this can work in practice.

For convenience, we will start by redefining the display() functionality:

In [133]:
class display(object):
    """Display HTML representation of multiple objects"""
    template = """<div style="float: left; padding: 10px;">
    <p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
    </div>"""
    def __init__(self, *args):
        self.args = args
        
    def _repr_html_(self):
        return '\n'.join(self.template.format(a, eval(a)._repr_html_())
                         for a in self.args)
    
    def __repr__(self):
        return '\n\n'.join(a + '\n' + repr(eval(a))
                           for a in self.args)

Relational Algebra¶

The behavior implemented in pd.merge() is a subset of what is known as relational algebra, which is a formal set of rules for manipulating relational data, and forms the conceptual foundation of operations available in most databases. The strength of the relational algebra approach is that it proposes several primitive operations, which become the building blocks of more complicated operations on any dataset. With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed.

Pandas implements several of these fundamental building-blocks in the pd.merge() function and the related join() method of Series and Dataframes.

Categories of Joins¶

The pd.merge() function implements a number of types of joins: the one-to-one, many-to-one, and many-to-many joins. All three types of joins are accessed via an identical call to the pd.merge() interface; the type of join performed depends on the form of the input data.

One-to-one joins¶

Perhaps the simplest type of merge expresion is the one-to-one join, which is in many ways very similar to the column-wise concatenation that we have already seen. As a concrete example, consider the following two DataFrames which contain information on several employees in a company:

In [134]:
df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],
                    'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})
df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],
                    'hire_date': [2004, 2008, 2012, 2014]})
display('df1', 'df2')
Out[134]:

df1

employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering
3 Sue HR

df2

employee hire_date
0 Lisa 2004
1 Bob 2008
2 Jake 2012
3 Sue 2014

To combine this information into a single DataFrame, we can use the pd.merge() function:

In [135]:
df3 = pd.merge(df1, df2)
df3
Out[135]:
employee group hire_date
0 Bob Accounting 2008
1 Jake Engineering 2012
2 Lisa Engineering 2004
3 Sue HR 2014

The pd.merge() function recognizes that each DataFrame has an "employee" column, and automatically joins using this column as a key. The result of the merge is a new DataFrame that combines the information from the two inputs. Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the "employee" column differs between df1 and df2, and the pd.merge() function correctly accounts for this. Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the left_index and right_index keywords, discussed momentarily).

Many-to-one joins¶

Many-to-one joins are joins in which one of the two key columns contains duplicate entries. For the many-to-one case, the resulting DataFrame will preserve those duplicate entries as appropriate:

In [136]:
df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],
                    'supervisor': ['Carly', 'Guido', 'Steve']})
display('df3', 'df4', 'pd.merge(df3, df4)')
Out[136]:

df3

employee group hire_date
0 Bob Accounting 2008
1 Jake Engineering 2012
2 Lisa Engineering 2004
3 Sue HR 2014

df4

group supervisor
0 Accounting Carly
1 Engineering Guido
2 HR Steve

pd.merge(df3, df4)

employee group hire_date supervisor
0 Bob Accounting 2008 Carly
1 Jake Engineering 2012 Guido
2 Lisa Engineering 2004 Guido
3 Sue HR 2014 Steve

Many-to-many joins¶

Many-to-many joins are a bit confusing conceptually, but are nevertheless well defined. If the key column in both the left and right array contains duplicates, then the result is a many-to-many merge. Consider the following, where we have a DataFrame showing one or more skills associated with a particular group. By performing a many-to-many join, we can recover the skills associated with any individual person:

In [137]:
df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',
                              'Engineering', 'Engineering', 'HR', 'HR'],
                    'skills': ['math', 'spreadsheets', 'coding', 'linux',
                               'spreadsheets', 'organization']})
display('df1', 'df5', "pd.merge(df1, df5)")
Out[137]:

df1

employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering
3 Sue HR

df5

group skills
0 Accounting math
1 Accounting spreadsheets
2 Engineering coding
3 Engineering linux
4 HR spreadsheets
5 HR organization

pd.merge(df1, df5)

employee group skills
0 Bob Accounting math
1 Bob Accounting spreadsheets
2 Jake Engineering coding
3 Jake Engineering linux
4 Lisa Engineering coding
5 Lisa Engineering linux
6 Sue HR spreadsheets
7 Sue HR organization

Specification of the Merge Key¶

We've already seen the default behavior of pd.merge(): it looks for one or more matching column names between the two inputs, and uses this as the key. However, often the column names will not match so nicely, and pd.merge() provides a variety of options for handling this.

The on keyword¶

Most simply, you can explicitly specify the name of the key column using the on keyword, which takes a column name or a list of column names:

In [198]:
display('df1', 'df2', "pd.merge(df1, df2, on='employee')")
Out[198]:

df1

employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering
3 Sue HR

df2

employee hire_date
0 Lisa 2004
1 Bob 2008
2 Jake 2012
3 Sue 2014

pd.merge(df1, df2, on='employee')

employee group hire_date
0 Bob Accounting 2008
1 Jake Engineering 2012
2 Lisa Engineering 2004
3 Sue HR 2014

The left_on and right_on keywords¶

At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as "name" rather than "employee". In this case, we can use the left_on and right_on keywords to specify the two column names:

In [138]:
df3 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],
                    'salary': [70000, 80000, 120000, 90000]})
display('df1', 'df3', 'pd.merge(df1, df3, left_on="employee", right_on="name")')
Out[138]:

df1

employee group
0 Bob Accounting
1 Jake Engineering
2 Lisa Engineering
3 Sue HR

df3

name salary
0 Bob 70000
1 Jake 80000
2 Lisa 120000
3 Sue 90000

pd.merge(df1, df3, left_on="employee", right_on="name")

employee group name salary
0 Bob Accounting Bob 70000
1 Jake Engineering Jake 80000
2 Lisa Engineering Lisa 120000
3 Sue HR Sue 90000

The result has a redundant column that we can drop if desired–for example, by using the drop() method of DataFrames:

In [200]:
pd.merge(df1, df3, left_on="employee", right_on="name").drop('name', axis=1)
Out[200]:
employee group salary
0 Bob Accounting 70000
1 Jake Engineering 80000
2 Lisa Engineering 120000
3 Sue HR 90000

The left_index and right_index keywords¶

Sometimes, rather than merging on a column, you would instead like to merge on an index. For example, your data might look like this:

In [142]:
df1.set_index?
In [147]:
df1a = df1.set_index([ 'group', 'employee'])
df1a
Out[147]:
group employee
Accounting Bob
Engineering Jake
Lisa
HR Sue
In [139]:
df1a = df1.set_index(['employee', 'group'])
df2a = df2.set_index('employee')
display('df1a', 'df2a')
Out[139]:

df1a

group
employee
Bob Accounting
Jake Engineering
Lisa Engineering
Sue HR

df2a

hire_date
employee
Lisa 2004
Bob 2008
Jake 2012
Sue 2014

You can use the index as the key for merging by specifying the left_index and/or right_index flags in pd.merge():

In [140]:
display('df1a', 'df2a', "pd.merge(df1a, df2a, left_index=True, right_index=True)")
Out[140]:

df1a

group
employee
Bob Accounting
Jake Engineering
Lisa Engineering
Sue HR

df2a

hire_date
employee
Lisa 2004
Bob 2008
Jake 2012
Sue 2014

pd.merge(df1a, df2a, left_index=True, right_index=True)

group hire_date
employee
Bob Accounting 2008
Jake Engineering 2012
Lisa Engineering 2004
Sue HR 2014
In [141]:
df1a, df2a, pd.merge(df1a, df2a, left_index=True, right_index=True)
Out[141]:
(                group
 employee             
 Bob        Accounting
 Jake      Engineering
 Lisa      Engineering
 Sue                HR,
           hire_date
 employee           
 Lisa           2004
 Bob            2008
 Jake           2012
 Sue            2014,
                 group  hire_date
 employee                        
 Bob        Accounting       2008
 Jake      Engineering       2012
 Lisa      Engineering       2004
 Sue                HR       2014)

For convenience, DataFrames implement the join() method, which performs a merge that defaults to joining on indices:

In [203]:
display('df1a', 'df2a', 'df1a.join(df2a)')
Out[203]:

df1a

group
employee
Bob Accounting
Jake Engineering
Lisa Engineering
Sue HR

df2a

hire_date
employee
Lisa 2004
Bob 2008
Jake 2012
Sue 2014

df1a.join(df2a)

group hire_date
employee
Bob Accounting 2008
Jake Engineering 2012
Lisa Engineering 2004
Sue HR 2014

If you'd like to mix indices and columns, you can combine left_index with right_on or left_on with right_index to get the desired behavior:

In [204]:
display('df1a', 'df3', "pd.merge(df1a, df3, left_index=True, right_on='name')")
Out[204]:

df1a

group
employee
Bob Accounting
Jake Engineering
Lisa Engineering
Sue HR

df3

name salary
0 Bob 70000
1 Jake 80000
2 Lisa 120000
3 Sue 90000

pd.merge(df1a, df3, left_index=True, right_on='name')

group name salary
0 Accounting Bob 70000
1 Engineering Jake 80000
2 Engineering Lisa 120000
3 HR Sue 90000

Specifying Set Arithmetic for Joins¶

We have glossed over one important consideration in performing a join: the type of set arithmetic used in the join. This comes up when a value appears in one key column but not the other:

In [149]:
df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'], 'food': ['fish', 'beans', 'bread']},
                   columns=['name', 'food'])
df7 = pd.DataFrame({'name': ['Mary', 'Joseph'], 'drink': ['wine', 'beer']},
                   columns=['name', 'drink'])
display('df6', 'df7', 'pd.merge(df6, df7)')
Out[149]:

df6

name food
0 Peter fish
1 Paul beans
2 Mary bread

df7

name drink
0 Mary wine
1 Joseph beer

pd.merge(df6, df7)

name food drink
0 Mary bread wine

Here we have merged two datasets that have only a single "name" entry in common: Mary. By default, the result contains the intersection of the two sets of inputs; this is what is known as an inner join. We can specify this explicitly using the how keyword, which defaults to "inner":

In [150]:
pd.merge(df6, df7, how='inner')
Out[150]:
name food drink
0 Mary bread wine

Other options for the how keyword are 'outer', 'left', and 'right'. An outer join returns a join over the union of the input columns, and fills in all missing values with NAs:

In [151]:
display('df6', 'df7', "pd.merge(df6, df7, how='outer')")
Out[151]:

df6

name food
0 Peter fish
1 Paul beans
2 Mary bread

df7

name drink
0 Mary wine
1 Joseph beer

pd.merge(df6, df7, how='outer')

name food drink
0 Peter fish NaN
1 Paul beans NaN
2 Mary bread wine
3 Joseph NaN beer

The left join and right join return joins over the left entries and right entries, respectively:

In [152]:
display('df6', 'df7', "pd.merge(df6, df7, how='left')")
Out[152]:

df6

name food
0 Peter fish
1 Paul beans
2 Mary bread

df7

name drink
0 Mary wine
1 Joseph beer

pd.merge(df6, df7, how='left')

name food drink
0 Peter fish NaN
1 Paul beans NaN
2 Mary bread wine
In [153]:
display('df6', 'df7', "pd.merge(df6, df7, how='right')")
Out[153]:

df6

name food
0 Peter fish
1 Paul beans
2 Mary bread

df7

name drink
0 Mary wine
1 Joseph beer

pd.merge(df6, df7, how='right')

name food drink
0 Mary bread wine
1 Joseph NaN beer
In [154]:
pd.merge?

Overlapping Column Names: The suffixes Keyword¶

Finally, you may end up in a case where your two input DataFrames have conflicting column names:

In [210]:
df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],
                    'rank': [1, 2, 3, 4]})
df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],
                    'rank': [3, 1, 4, 2]})
display('df8', 'df9', 'pd.merge(df8, df9, on="name")')
Out[210]:

df8

name rank
0 Bob 1
1 Jake 2
2 Lisa 3
3 Sue 4

df9

name rank
0 Bob 3
1 Jake 1
2 Lisa 4
3 Sue 2

pd.merge(df8, df9, on="name")

name rank_x rank_y
0 Bob 1 3
1 Jake 2 1
2 Lisa 3 4
3 Sue 4 2

Because the output would have two conflicting column names, the merge function automatically appends a suffix _x or _y to make the output columns unique. If these defaults are inappropriate, it is possible to specify a custom suffix using the suffixes keyword:

In [211]:
display('df8', 'df9', 'pd.merge(df8, df9, on="name", suffixes=["_L", "_R"])')
Out[211]:

df8

name rank
0 Bob 1
1 Jake 2
2 Lisa 3
3 Sue 4

df9

name rank
0 Bob 3
1 Jake 1
2 Lisa 4
3 Sue 2

pd.merge(df8, df9, on="name", suffixes=["_L", "_R"])

name rank_L rank_R
0 Bob 1 3
1 Jake 2 1
2 Lisa 3 4
3 Sue 4 2

Example: US States Data¶

Merge and join operations come up most often when combining data from different sources. Here we will consider an example of some data about US states and their populations. The data files can be found at http://github.com/jakevdp/data-USstates/:

In [221]:
pop = pd.read_csv('data/state-population.csv')
areas = pd.read_csv('data/state-areas.csv')
abbrevs = pd.read_csv('data/state-abbrevs.csv')

display('pop.head()', 'areas.head()', 'abbrevs.head()')
Out[221]:

pop.head()

state/region ages year population
0 AL under18 2012 1117489.0
1 AL total 2012 4817528.0
2 AL under18 2010 1130966.0
3 AL total 2010 4785570.0
4 AL under18 2011 1125763.0

areas.head()

state area (sq. mi)
0 Alabama 52423
1 Alaska 656425
2 Arizona 114006
3 Arkansas 53182
4 California 163707

abbrevs.head()

state abbreviation
0 Alabama AL
1 Alaska AK
2 Arizona AZ
3 Arkansas AR
4 California CA

Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density. We clearly have the data here to find this result, but we'll have to combine the datasets to find the result.

We'll start with a many-to-one merge that will give us the full state name within the population DataFrame. We want to merge based on the state/region column of pop, and the abbreviation column of abbrevs. We'll use how='outer' to make sure no data is thrown away due to mismatched labels.

In [222]:
merged = pd.merge(pop, abbrevs, how='outer',
                  left_on='state/region', right_on='abbreviation')
merged = merged.drop('abbreviation', 1) # drop duplicate info
merged.head()
Out[222]:
state/region ages year population state
0 AL under18 2012 1117489.0 Alabama
1 AL total 2012 4817528.0 Alabama
2 AL under18 2010 1130966.0 Alabama
3 AL total 2010 4785570.0 Alabama
4 AL under18 2011 1125763.0 Alabama

Let's double-check whether there were any mismatches here, which we can do by looking for rows with nulls:

In [223]:
merged.isnull().any()
Out[223]:
state/region    False
ages            False
year            False
population       True
state            True
dtype: bool

Some of the population info is null:

In [224]:
merged[merged['population'].isnull()].head()
Out[224]:
state/region ages year population state
2448 PR under18 1990 NaN NaN
2449 PR total 1990 NaN NaN
2450 PR total 1991 NaN NaN
2451 PR under18 1991 NaN NaN
2452 PR total 1993 NaN NaN

It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available from the original source.

More importantly, we see also that some of the new state entries are also null, which means that there was no corresponding entry in the abbrevs key! Let's figure out which regions lack this match:

In [225]:
merged.loc[merged['state'].isnull(), 'state/region'].unique()
Out[225]:
array(['PR', 'USA'], dtype=object)

We can quickly infer the issue: our population data includes entries for Puerto Rico (PR) and the United States as a whole (USA), while these entries do not appear in the state abbreviation key. We can fix these quickly by filling in appropriate entries:

In [226]:
merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'
merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'
merged.isnull().any()
Out[226]:
state/region    False
ages            False
year            False
population       True
state           False
dtype: bool

No more nulls in the state column: we're all set!

Now we can merge the result with the area data using a similar procedure. Examining our results, we will want to join on the state column in both:

In [227]:
final = pd.merge(merged, areas, on='state', how='left')
final.head()
Out[227]:
state/region ages year population state area (sq. mi)
0 AL under18 2012 1117489.0 Alabama 52423.0
1 AL total 2012 4817528.0 Alabama 52423.0
2 AL under18 2010 1130966.0 Alabama 52423.0
3 AL total 2010 4785570.0 Alabama 52423.0
4 AL under18 2011 1125763.0 Alabama 52423.0

Again, let's check for nulls to see if there were any mismatches:

In [228]:
final.isnull().any()
Out[228]:
state/region     False
ages             False
year             False
population        True
state            False
area (sq. mi)     True
dtype: bool

There are nulls in the area column; we can take a look to see which regions were ignored here:

In [230]:
final['state'][final['area (sq. mi)'].isnull()].unique()
Out[230]:
array(['United States'], dtype=object)

We see that our areas DataFrame does not contain the area of the United States as a whole. We could insert the appropriate value (using the sum of all state areas, for instance), but in this case we'll just drop the null values because the population density of the entire United States is not relevant to our current discussion:

In [231]:
final.dropna(inplace=True)
final.head()
Out[231]:
state/region ages year population state area (sq. mi)
0 AL under18 2012 1117489.0 Alabama 52423.0
1 AL total 2012 4817528.0 Alabama 52423.0
2 AL under18 2010 1130966.0 Alabama 52423.0
3 AL total 2010 4785570.0 Alabama 52423.0
4 AL under18 2011 1125763.0 Alabama 52423.0

Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2000, and the total population. We'll use the query() function to do this quickly:

In [232]:
data2010 = final.query("year == 2010 & ages == 'total'")
data2010.head()
Out[232]:
state/region ages year population state area (sq. mi)
3 AL total 2010 4785570.0 Alabama 52423.0
91 AK total 2010 713868.0 Alaska 656425.0
101 AZ total 2010 6408790.0 Arizona 114006.0
189 AR total 2010 2922280.0 Arkansas 53182.0
197 CA total 2010 37333601.0 California 163707.0

Now let's compute the population density and display it in order. We'll start by re-indexing our data on the state, and then compute the result:

In [233]:
data2010.set_index('state', inplace=True)
density = data2010['population'] / data2010['area (sq. mi)']
In [234]:
density.sort_values(ascending=False, inplace=True)
density.head()
Out[234]:
state
District of Columbia    8898.897059
Puerto Rico             1058.665149
New Jersey              1009.253268
Rhode Island             681.339159
Connecticut              645.600649
dtype: float64

The result is a ranking of US states plus Washington, DC, and Puerto Rico in order of their 2010 population density, in residents per square mile. We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey.

We can also check the end of the list:

In [235]:
density.tail()
Out[235]:
state
South Dakota    10.583512
North Dakota     9.537565
Montana          6.736171
Wyoming          5.768079
Alaska           1.087509
dtype: float64

We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile.

This type of messy data merging is a common task when trying to answer questions using real-world data sources.

Aggregation and Grouping¶

An essential piece of analysis of large data is efficient summarization: computing aggregations like sum(), mean(), median(), min(), and max(), in which a single number gives insight into the nature of a potentially large dataset.

we'll use the same display magic function as usual:

In [90]:
class display(object):
    """Display HTML representation of multiple objects"""
    template = """<div style="float: left; padding: 10px;">
    <p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
    </div>"""
    def __init__(