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Python Data Science Handbook

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  1. Introduktion
  2. IPython: Beyond Normal Python
    8 Ämnen
  3. Introduction to NumPy
    9 Ämnen
  4. Data Manipulation with Pandas
    13 Ämnen
  5. Visualization with Matplotlib
    15 Ämnen
  6. Machine Learning
    15 Ämnen
  7. Appendix: Figure Code
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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.

We begin with the standard imports:In [1]:

import pandas as pd
import numpy as np

For convenience, we’ll define this function which creates a DataFrame of a particular form that will be useful below:In [2]:

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[2]:

ABC
0A0B0C0
1A1B1C1
2A2B2C2

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 [3]:

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)
    

The use of this will become clearer as we continue our discussion in the following section.

Recall: Concatenation of NumPy Arrays

Concatenation of Series and DataFrame objects is very similar to concatenation of Numpy arrays, which can be done via the np.concatenate function as discussed in The Basics of NumPy Arrays. Recall that with it, you can combine the contents of two or more arrays into a single array:In [4]:

x = [1, 2, 3]
y = [4, 5, 6]
z = [7, 8, 9]
np.concatenate([x, y, z])

Out[4]:

array([1, 2, 3, 4, 5, 6, 7, 8, 9])

The first argument is a list or tuple of arrays to concatenate. Additionally, it takes an axis keyword that allows you to specify the axis along which the result will be concatenated:In [5]:

x = [[1, 2],
     [3, 4]]
np.concatenate([x, x], axis=1)

Out[5]:

array([[1, 2, 1, 2],
       [3, 4, 3, 4]])

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 DataFrameobjects, just as np.concatenate() can be used for simple concatenations of arrays:In [6]:

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[6]:

1    A
2    B
3    C
4    D
5    E
6    F
dtype: object

It also works to concatenate higher-dimensional objects, such as DataFrames:In [7]:

df1 = make_df('AB', [1, 2])
df2 = make_df('AB', [3, 4])
display('df1', 'df2', 'pd.concat([df1, df2])')

Out[7]:

df1

AB
1A1B1
2A2B2

df2

AB
3A3B3
4A4B4

pd.concat([df1, df2])

AB
1A1B1
2A2B2
3A3B3
4A4B4

By default, the concatenation takes place row-wise within the DataFrame (i.e., axis=0). Like np.concatenatepd.concat allows specification of an axis along which concatenation will take place. Consider the following example:In [8]:

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

Out[8]:

df3

AB
0A0B0
1A1B1

df4

CD
0C0D0
1C1D1

pd.concat([df3, df4], axis=’col’)

ABCD
0A0B0C0D0
1A1B1C1D1

We could have equivalently specified axis=1; here we’ve used the more intuitive axis='col'.

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! Consider this simple example:In [9]:

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[9]:

x

AB
0A0B0
1A1B1

y

AB
0A2B2
1A3B3

pd.concat([x, y])

AB
0A0B0
1A1B1
0A2B2
1A3B3

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.

Catching the repeats as an error

If you’d like to simply verify that the indices in the result of pd.concat() do not overlap, you can specify the verify_integrity flag. With this set to True, the concatenation will raise an exception if there are duplicate indices. Here is an example, where for clarity we’ll catch and print the error message:In [10]:

try:
    pd.concat([x, y], verify_integrity=True)
except ValueError as e:
    print("ValueError:", e)
ValueError: Indexes have overlapping values: [0, 1]

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 [11]:

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

Out[11]:

x

AB
0A0B0
1A1B1

y

AB
0A2B2
1A3B3

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

AB
0A0B0
1A1B1
2A2B2
3A3B3

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 [12]:

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

Out[12]:

x

AB
0A0B0
1A1B1

y

AB
0A2B2
1A3B3

pd.concat([x, y], keys=[’x’, ’y’])

AB
x0A0B0
1A1B1
y0A2B2
1A3B3

The result is a multiply indexed DataFrame, and we can use the tools discussed in Hierarchical Indexing to transform this data into the representation we’re interested in.

Concatenation with joins

In the simple examples we just looked at, we were mainly concatenating DataFrames with shared column names. 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 [13]:

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

Out[13]:

df5

ABC
1A1B1C1
2A2B2C2

df6

BCD
3B3C3D3
4B4C4D4

pd.concat([df5, df6])

ABCD
1A1B1C1NaN
2A2B2C2NaN
3NaNB3C3D3
4NaNB4C4D4

By default, the entries for which no data is available are filled with NA values. To change this, we can specify one of several options for the join and join_axesparameters of the concatenate function. By default, the join is a union of the input columns (join='outer'), but we can change this to an intersection of the columns using join='inner':In [14]:

display('df5', 'df6',
        "pd.concat([df5, df6], join='inner')")

Out[14]:

df5

ABC
1A1B1C1
2A2B2C2

df6

BCD
3B3C3D3
4B4C4D4

pd.concat([df5, df6], join=’inner’)

BC
1B1C1
2B2C2
3B3C3
4B4C4

Another option is to directly specify the index of the remaininig colums using the join_axes argument, which takes a list of index objects. Here we’ll specify that the returned columns should be the same as those of the first input:In [15]:

display('df5', 'df6',
        "pd.concat([df5, df6], join_axes=[df5.columns])")

Out[15]:

df5

ABC
1A1B1C1
2A2B2C2

df6

BCD
3B3C3D3
4B4C4D4

pd.concat([df5, df6], join_axes=[df5.columns])

ABC
1A1B1C1
2A2B2C2
3NaNB3C3
4NaNB4C4

The combination of options of the pd.concat function allows a wide range of possible behaviors when joining two datasets; keep these in mind as you use these tools for your own data.

The append() method

Because direct array concatenation is so common, Series and DataFrameobjects 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 [16]:

display('df1', 'df2', 'df1.append(df2)')

Out[16]:

df1

AB
1A1B1
2A2B2

df2

AB
3A3B3
4A4B4

df1.append(df2)

AB
1A1B1
2A2B2
3A3B3
4A4B4

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.

In the next section, we’ll look at another more powerful approach to combining data from multiple sources, the database-style merges/joins implemented in pd.merge. For more information on concat()append(), and related functionality, see the ”Merge, Join, and Concatenate” section of the Pandas documentation.