# 7 Data Cleaning and Preparation

During the course of doing data analysis and modeling, a significant amount of time is spent on data preparation: loading, cleaning, transforming, and rearranging. Such tasks are often reported to take up 80% or more of an analyst's time. Sometimes the way that data is stored in files or databases is not in the right format for a particular task. Many researchers choose to do ad hoc processing of data from one form to another using a general-purpose programming language, like Python, Perl, R, or Java, or Unix text-processing tools like sed or awk. Fortunately, pandas, along with the built-in Python language features, provides you with a high-level, flexible, and fast set of tools to enable you to manipulate data into the right form.

If you identify a type of data manipulation that isn’t anywhere in this book or elsewhere in the pandas library, feel free to share your use case on one of the Python mailing lists or on the pandas GitHub site. Indeed, much of the design and implementation of pandas has been driven by the needs of real-world applications.

In this chapter I discuss tools for missing data, duplicate data, string manipulation, and some other analytical data transformations. In the next chapter, I focus on combining and rearranging datasets in various ways.

```
12]: import numpy as np
In [
13]: import pandas as pd In [
```

## 7.1 Handling Missing Data

Missing data occurs commonly in many data analysis applications. One of the goals of pandas is to make working with missing data as painless as possible. For example, all of the descriptive statistics on pandas objects exclude missing data by default.

The way that missing data is represented in pandas objects is somewhat imperfect, but it is sufficient for most real world use. For data with `float64`

dtype, pandas uses the floating-point value `NaN`

(Not a Number) to represent missing data. We call this a *sentinel value*: when present, it indicates a missing (or *null*) value:

```
14]: float_data = pd.Series([1.2, -3.5, np.nan, 0])
In [
15]: float_data
In [15]:
Out[0 1.2
1 -3.5
2 NaN
3 0.0
dtype: float64
```

The `isna`

method gives us a boolean Series with `True`

where values are null:

```
16]: float_data.isna()
In [16]:
Out[0 False
1 False
2 True
3 False
bool dtype:
```

In pandas, we've adopted a convention used in the R programming language by referring to missing data as NA, which stands for *not available*. In statistics applications, NA data may either be data that does not exist or that exists but was not observed (through problems with data collection, for example). When cleaning up data for analysis, it is often important to do analysis on the missing data itself to identify data collection problems or potential biases in the data caused by missing data.

The built-in Python `None`

value is also treated as NA:

```
17]: string_data = pd.Series(["aardvark", np.nan, None, "avocado"])
In [
18]: string_data
In [18]:
Out[0 aardvark
1 NaN
2 None
3 avocado
object
dtype:
19]: string_data.isna()
In [19]:
Out[0 False
1 True
2 True
3 False
bool
dtype:
20]: float_data = pd.Series([1, 2, None], dtype='float64')
In [
21]: float_data
In [21]:
Out[0 1.0
1 2.0
2 NaN
dtype: float64
22]: float_data.isna()
In [22]:
Out[0 False
1 False
2 True
bool dtype:
```

The pandas project has attempted to make working with missing data handling consistent across data types. Functions like `pandas.isna`

, abstract away many of the annoying details. See Table 7.1 for a list of some functions related to missing data handling.

Method | Description |
---|---|

`dropna` |
Filter axis labels based on whether values for each label have missing data, with varying thresholds for how much missing data to tolerate. |

`fillna` |
Fill in missing data with some value or using an interpolation method such as `"ffill"` or `"bfill"` . |

`isna` |
Return boolean values indicating which values are missing/NA. |

`notna` |
Negation of `isna` , returns `True` for non-NA values and `False` for NA values. |

### Filtering Out Missing Data

There are a few ways to filter out missing data. While you always have the option to do it by hand using `pandas.isna`

and boolean indexing, the `dropna`

can be helpful. On a Series, it returns the Series with only the non-null data and index values:

```
23]: data = pd.Series([1, np.nan, 3.5, np.nan, 7])
In [
24]: data.dropna()
In [24]:
Out[0 1.0
2 3.5
4 7.0
dtype: float64
```

This is the same things as doing:

```
25]: data[data.notna()]
In [25]:
Out[0 1.0
2 3.5
4 7.0
dtype: float64
```

With DataFrame objects, there are different ways you may need to remove missing data. You may want to drop rows or columns that are all NA or only those rows or columns containing any NAs at all. `dropna`

by default drops any row containing a missing value:

```
26]: data = pd.DataFrame([[1., 6.5, 3.], [1., np.nan, np.nan],
In [6.5, 3.]])
....: [np.nan, np.nan, np.nan], [np.nan,
27]: data
In [27]:
Out[0 1 2
0 1.0 6.5 3.0
1 1.0 NaN NaN
2 NaN NaN NaN
3 NaN 6.5 3.0
28]: data.dropna()
In [28]:
Out[0 1 2
0 1.0 6.5 3.0
```

Passing `how="all"`

will only drop rows that are all NA:

```
29]: data.dropna(how="all")
In [29]:
Out[0 1 2
0 1.0 6.5 3.0
1 1.0 NaN NaN
3 NaN 6.5 3.0
```

Keep in mind that these functions return new objects by default and do not modify the contents of the original object.

To drop columns in the same way, pass `axis="columns"`

:

```
30]: data[4] = np.nan
In [
31]: data
In [31]:
Out[0 1 2 4
0 1.0 6.5 3.0 NaN
1 1.0 NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN 6.5 3.0 NaN
32]: data.dropna(axis="columns", how="all")
In [32]:
Out[0 1 2
0 1.0 6.5 3.0
1 1.0 NaN NaN
2 NaN NaN NaN
3 NaN 6.5 3.0
```

Suppose you want to keep only rows containing at most a certain number of missing observations. You can indicate this with the `thresh`

argument:

```
33]: df = pd.DataFrame(np.random.standard_normal((7, 3)))
In [
34]: df.iloc[:4, 1] = np.nan
In [
35]: df.iloc[:2, 2] = np.nan
In [
36]: df
In [36]:
Out[0 1 2
0 -0.204708 NaN NaN
1 -0.555730 NaN NaN
2 0.092908 NaN 0.769023
3 1.246435 NaN -1.296221
4 0.274992 0.228913 1.352917
5 0.886429 -2.001637 -0.371843
6 1.669025 -0.438570 -0.539741
37]: df.dropna()
In [37]:
Out[0 1 2
4 0.274992 0.228913 1.352917
5 0.886429 -2.001637 -0.371843
6 1.669025 -0.438570 -0.539741
38]: df.dropna(thresh=2)
In [38]:
Out[0 1 2
2 0.092908 NaN 0.769023
3 1.246435 NaN -1.296221
4 0.274992 0.228913 1.352917
5 0.886429 -2.001637 -0.371843
6 1.669025 -0.438570 -0.539741
```

### Filling In Missing Data

Rather than filtering out missing data (and potentially discarding other data along with it), you may want to fill in the “holes” in any number of ways. For most purposes, the `fillna`

method is the workhorse function to use. Calling `fillna`

with a constant replaces missing values with that value:

```
39]: df.fillna(0)
In [39]:
Out[0 1 2
0 -0.204708 0.000000 0.000000
1 -0.555730 0.000000 0.000000
2 0.092908 0.000000 0.769023
3 1.246435 0.000000 -1.296221
4 0.274992 0.228913 1.352917
5 0.886429 -2.001637 -0.371843
6 1.669025 -0.438570 -0.539741
```

Calling `fillna`

with a dictionary, you can use a different fill value for each column:

```
40]: df.fillna({1: 0.5, 2: 0})
In [40]:
Out[0 1 2
0 -0.204708 0.500000 0.000000
1 -0.555730 0.500000 0.000000
2 0.092908 0.500000 0.769023
3 1.246435 0.500000 -1.296221
4 0.274992 0.228913 1.352917
5 0.886429 -2.001637 -0.371843
6 1.669025 -0.438570 -0.539741
```

The same interpolation methods available for reindexing (see ???) can be used with `fillna`

:

```
41]: df = pd.DataFrame(np.random.standard_normal((6, 3)))
In [
42]: df.iloc[2:, 1] = np.nan
In [
43]: df.iloc[4:, 2] = np.nan
In [
44]: df
In [44]:
Out[0 1 2
0 0.476985 3.248944 -1.021228
1 -0.577087 0.124121 0.302614
2 0.523772 NaN 1.343810
3 -0.713544 NaN -2.370232
4 -1.860761 NaN NaN
5 -1.265934 NaN NaN
45]: df.fillna(method="ffill")
In [45]:
Out[0 1 2
0 0.476985 3.248944 -1.021228
1 -0.577087 0.124121 0.302614
2 0.523772 0.124121 1.343810
3 -0.713544 0.124121 -2.370232
4 -1.860761 0.124121 -2.370232
5 -1.265934 0.124121 -2.370232
46]: df.fillna(method="ffill", limit=2)
In [46]:
Out[0 1 2
0 0.476985 3.248944 -1.021228
1 -0.577087 0.124121 0.302614
2 0.523772 0.124121 1.343810
3 -0.713544 0.124121 -2.370232
4 -1.860761 NaN -2.370232
5 -1.265934 NaN -2.370232
```

With `fillna`

you can do lots of other things such as simple data imputation using the median or mean statistics:

```
47]: data = pd.Series([1., np.nan, 3.5, np.nan, 7])
In [
48]: data.fillna(data.mean())
In [48]:
Out[0 1.000000
1 3.833333
2 3.500000
3 3.833333
4 7.000000
dtype: float64
```

See Table 7.3 for a reference on `fillna`

.

Argument | Description |
---|---|

`value` |
Scalar value or dictionary-like object to use to fill missing values |

`method` |
Interpolation method; one of `"bfill"` (backward fill), `"ffill"` (forward fill). Default is `None` |

`axis` |
Axis to fill on (`"index"` or `"columns"` ); default `axis="index"` |

`limit` |
For forward and backward filling, maximum number of consecutive periods to fill |

## 7.2 Data Transformation

So far in this chapter we’ve been concerned with handling missing data. Filtering, cleaning, and other transformations are another class of important operations.

### Removing Duplicates

Duplicate rows may be found in a DataFrame for any number of reasons. Here is an example:

```
49]: data = pd.DataFrame({"k1": ["one", "two"] * 3 + ["two"],
In ["k2": [1, 1, 2, 3, 3, 4, 4]})
....:
50]: data
In [50]:
Out[
k1 k20 one 1
1 two 1
2 one 2
3 two 3
4 one 3
5 two 4
6 two 4
```

The DataFrame method `duplicated`

returns a boolean Series indicating whether each row is a duplicate (its column values are exactly equal to those in an earlier row) or not:

```
51]: data.duplicated()
In [51]:
Out[0 False
1 False
2 False
3 False
4 False
5 False
6 True
bool dtype:
```

Relatedly, `drop_duplicates`

returns a DataFrame with rows where the `duplicated`

array is `False`

filtered out:

```
52]: data.drop_duplicates()
In [52]:
Out[
k1 k20 one 1
1 two 1
2 one 2
3 two 3
4 one 3
5 two 4
```

Both of these methods by default consider all of the columns; alternatively, you can specify any subset of them to detect duplicates. Suppose we had an additional column of values and wanted to filter duplicates only based on the `"k1"`

column:

```
53]: data["v1"] = range(7)
In [
54]: data
In [54]:
Out[
k1 k2 v10 one 1 0
1 two 1 1
2 one 2 2
3 two 3 3
4 one 3 4
5 two 4 5
6 two 4 6
55]: data.drop_duplicates(subset=["k1"])
In [55]:
Out[
k1 k2 v10 one 1 0
1 two 1 1
```

`duplicated`

and `drop_duplicates`

by default keep the first observed value combination. Passing `keep="last"`

will return the last one:

```
56]: data.drop_duplicates(["k1", "k2"], keep="last")
In [56]:
Out[
k1 k2 v10 one 1 0
1 two 1 1
2 one 2 2
3 two 3 3
4 one 3 4
6 two 4 6
```

### Transforming Data Using a Function or Mapping

For many datasets, you may wish to perform some transformation based on the values in an array, Series, or column in a DataFrame. Consider the following hypothetical data collected about various kinds of meat:

```
57]: data = pd.DataFrame({"food": ["bacon", "pulled pork", "bacon",
In ["pastrami", "corned beef", "bacon",
....: "pastrami", "honey ham", "nova lox"],
....: "ounces": [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})
....:
58]: data
In [58]:
Out[
food ounces0 bacon 4.0
1 pulled pork 3.0
2 bacon 12.0
3 pastrami 6.0
4 corned beef 7.5
5 bacon 8.0
6 pastrami 3.0
7 honey ham 5.0
8 nova lox 6.0
```

Suppose you wanted to add a column indicating the type of animal that each food came from. Let’s write down a mapping of each distinct meat type to the kind of animal:

```
= {
meat_to_animal "bacon": "pig",
"pulled pork": "pig",
"pastrami": "cow",
"corned beef": "cow",
"honey ham": "pig",
"nova lox": "salmon"
}
```

The `map`

method on a Series (also discussed in Ch 5.2.5: Function Application and Mapping) accepts a function or dictionary-like object containing a mapping to do the transformation of values:

```
60]: data["animal"] = data["food"].map(meat_to_animal)
In [
61]: data
In [61]:
Out[
food ounces animal0 bacon 4.0 pig
1 pulled pork 3.0 pig
2 bacon 12.0 pig
3 pastrami 6.0 cow
4 corned beef 7.5 cow
5 bacon 8.0 pig
6 pastrami 3.0 cow
7 honey ham 5.0 pig
8 nova lox 6.0 salmon
```

We could also have passed a function that does all the work:

```
62]: def get_animal(x):
In [return meat_to_animal[x]
....:
63]: data["food"].map(get_animal)
In [63]:
Out[0 pig
1 pig
2 pig
3 cow
4 cow
5 pig
6 cow
7 pig
8 salmon
object Name: food, dtype:
```

Using `map`

is a convenient way to perform element-wise transformations and other data cleaning–related operations.

### Replacing Values

Filling in missing data with the `fillna`

method is a special case of more general value replacement. As you've already seen, `map`

can be used to modify a subset of values in an object but `replace`

provides a simpler and more flexible way to do so. Let’s consider this Series:

```
64]: data = pd.Series([1., -999., 2., -999., -1000., 3.])
In [
65]: data
In [65]:
Out[0 1.0
1 -999.0
2 2.0
3 -999.0
4 -1000.0
5 3.0
dtype: float64
```

The `-999`

values might be sentinel values for missing data. To replace these with NA values that pandas understands, we can use `replace`

, producing a new Series:

```
66]: data.replace(-999, np.nan)
In [66]:
Out[0 1.0
1 NaN
2 2.0
3 NaN
4 -1000.0
5 3.0
dtype: float64
```

If you want to replace multiple values at once, you instead pass a list and then the substitute value:

```
67]: data.replace([-999, -1000], np.nan)
In [67]:
Out[0 1.0
1 NaN
2 2.0
3 NaN
4 NaN
5 3.0
dtype: float64
```

To use a different replacement for each value, pass a list of substitutes:

```
68]: data.replace([-999, -1000], [np.nan, 0])
In [68]:
Out[0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
```

The argument passed can also be a dictionary:

```
69]: data.replace({-999: np.nan, -1000: 0})
In [69]:
Out[0 1.0
1 NaN
2 2.0
3 NaN
4 0.0
5 3.0
dtype: float64
```

### Renaming Axis Indexes

Like values in a Series, axis labels can be similarly transformed by a function or mapping of some form to produce new, differently labeled objects. You can also modify the axes in-place without creating a new data structure. Here’s a simple example:

```
70]: data = pd.DataFrame(np.arange(12).reshape((3, 4)),
In [=["Ohio", "Colorado", "New York"],
....: index=["one", "two", "three", "four"]) ....: columns
```

Like a Series, the axis indexes have a `map`

method:

```
71]: def transform(x):
In [return x[:4].upper()
....:
72]: data.index.map(transform)
In [72]: Index(['OHIO', 'COLO', 'NEW '], dtype='object') Out[
```

You can assign to the `index`

attribute, modifying the DataFrame in-place:

```
73]: data.index = data.index.map(transform)
In [
74]: data
In [74]:
Out[
one two three four0 1 2 3
OHIO 4 5 6 7
COLO 8 9 10 11 NEW
```

If you want to create a transformed version of a dataset without modifying the original, a useful method is `rename`

:

```
75]: data.rename(index=str.title, columns=str.upper)
In [75]:
Out[
ONE TWO THREE FOUR0 1 2 3
Ohio 4 5 6 7
Colo 8 9 10 11 New
```

Notably, `rename`

can be used in conjunction with a dictionary-like object providing new values for a subset of the axis labels:

```
76]: data.rename(index={"OHIO": "INDIANA"},
In [={"three": "peekaboo"})
....: columns76]:
Out[
one two peekaboo four0 1 2 3
INDIANA 4 5 6 7
COLO 8 9 10 11 NEW
```

`rename`

saves you from the chore of copying the DataFrame manually and assigning to its `index`

and `columns`

attributes.

### Discretization and Binning

Continuous data is often discretized or otherwise separated into “bins” for analysis. Suppose you have data about a group of people in a study, and you want to group them into discrete age buckets:

`77]: ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32] In [`

Let’s divide these into bins of 18 to 25, 26 to 35, 36 to 60, and finally 61 and older. To do so, you have to use `pandas.cut`

:

```
78]: bins = [18, 25, 35, 60, 100]
In [
79]: age_categories = pd.cut(ages, bins)
In [
80]: age_categories
In [80]:
Out[18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35,
[(60], (35, 60], (25, 35]]
12
Length: 4, interval[int64, right]): [(18, 25] < (25, 35] < (35, 60] < (60, 10
Categories (0]]
```

The object pandas returns is a special `Categorical`

object. The output you see describes the bins computed by `pandas.cut`

. Each bin is identified by a special (unique to pandas) interval value type containing the lower and upper limit of each bin:

```
81]: age_categories.codes
In [81]: array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)
Out[
82]: age_categories.categories
In [82]: IntervalIndex([(18, 25], (25, 35], (35, 60], (60, 100]], dtype='interval
Out[[int64, right]')
In [83]: age_categories.categories[0]
Out[83]: Interval(18, 25, closed='right')
In [84]: pd.value_counts(age_categories)
Out[84]:
(18, 25] 5
(25, 35] 3
(35, 60] 3
(60, 100] 1
dtype: int64
```

Note that `pd.value_counts(categories)`

are the bin counts for the result of `pandas.cut`

.

In the string representation of an interval, a parenthesis means that the side is *open* (exclusive), while the square bracket means it is *closed* (inclusive). You can change which side is closed by passing `right=False`

:

```
85]: pd.cut(ages, bins, right=False)
In [85]:
Out[18, 25), [18, 25), [25, 35), [25, 35), [18, 25), ..., [25, 35), [60, 100), [35,
[[60), [35, 60), [25, 35)]
12
Length: 4, interval[int64, left]): [[18, 25) < [25, 35) < [35, 60) < [60, 100
Categories ( )]
```

You can override the default interval-based bin labeling by passing a list or array to the `labels`

option:

```
86]: group_names = ["Youth", "YoungAdult", "MiddleAged", "Senior"]
In [
87]: pd.cut(ages, bins, labels=group_names)
In [87]:
Out['Youth', 'Youth', 'Youth', 'YoungAdult', 'Youth', ..., 'YoungAdult', 'Senior', '
[MiddleAged', 'MiddleAged', 'YoungAdult']
Length: 12
Categories (4, object): ['Youth' < 'YoungAdult' < 'MiddleAged' < 'Senior']
```

If you pass an integer number of bins to `pandas.cut`

instead of explicit bin edges, it will compute equal-length bins based on the minimum and maximum values in the data. Consider the case of some uniformly distributed data chopped into fourths:

```
88]: data = np.random.uniform(size=20)
In [
89]: pd.cut(data, 4, precision=2)
In [89]:
Out[0.34, 0.55], (0.34, 0.55], (0.76, 0.97], (0.76, 0.97], (0.34, 0.55], ..., (0.34
[(0.55], (0.34, 0.55], (0.55, 0.76], (0.34, 0.55], (0.12, 0.34]]
, 20
Length: 4, interval[float64, right]): [(0.12, 0.34] < (0.34, 0.55] < (0.55, 0
Categories (.76] <
0.76, 0.97]] (
```

The `precision=2`

option limits the decimal precision to two digits.

A closely related function, `pandas.qcut`

, bins the data based on sample quantiles. Depending on the distribution of the data, using `pandas.cut`

will not usually result in each bin having the same number of data points. Since `pandas.qcut`

uses sample quantiles instead, you will obtain roughly equally-sized bins:

```
90]: data = np.random.standard_normal(1000)
In [
91]: quartiles = pd.qcut(data, 4, precision=2)
In [
92]: quartiles
In [92]:
Out[-0.026, 0.62], (0.62, 3.93], (-0.68, -0.026], (0.62, 3.93], (-0.026, 0.62], ...
[(-0.68, -0.026], (-0.68, -0.026], (-2.96, -0.68], (0.62, 3.93], (-0.68, -0.026]
, (
]1000
Length: 4, interval[float64, right]): [(-2.96, -0.68] < (-0.68, -0.026] < (-0
Categories (.026, 0.62] <
0.62, 3.93]]
(
93]: pd.value_counts(quartiles)
In [93]:
Out[-2.96, -0.68] 250
(-0.68, -0.026] 250
(-0.026, 0.62] 250
(0.62, 3.93] 250
( dtype: int64
```

Similar to `pandas.cut`

you can pass your own quantiles (numbers between 0 and 1, inclusive):

```
94]: pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.]).value_counts()
In [94]:
Out[-2.9499999999999997, -1.187] 100
(-1.187, -0.0265] 400
(-0.0265, 1.286] 400
(1.286, 3.928] 100
( dtype: int64
```

We’ll return to `pandas.cut`

and `pandas.qcut`

later in the chapter during our discussion of aggregation and group operations, as these discretization functions are especially useful for quantile and group analysis.

### Detecting and Filtering Outliers

Filtering or transforming outliers is largely a matter of applying array operations. Consider a DataFrame with some normally distributed data:

```
95]: data = pd.DataFrame(np.random.standard_normal((1000, 4)))
In [
96]: data.describe()
In [96]:
Out[0 1 2 3
1000.000000 1000.000000 1000.000000 1000.000000
count 0.049091 0.026112 -0.002544 -0.051827
mean 0.996947 1.007458 0.995232 0.998311
std min -3.645860 -3.184377 -3.745356 -3.428254
25% -0.599807 -0.612162 -0.687373 -0.747478
50% 0.047101 -0.013609 -0.022158 -0.088274
75% 0.756646 0.695298 0.699046 0.623331
max 2.653656 3.525865 2.735527 3.366626
```

Suppose you wanted to find values in one of the columns exceeding 3 in absolute value:

```
97]: col = data[2]
In [
98]: col[col.abs() > 3]
In [98]:
Out[41 -3.399312
136 -3.745356
2, dtype: float64 Name:
```

To select all rows having a value exceeding 3 or –3, you can use the `any`

method on a boolean DataFrame:

```
99]: data[(data.abs() > 3).any(axis="columns")]
In [99]:
Out[0 1 2 3
41 0.457246 -0.025907 -3.399312 -0.974657
60 1.951312 3.260383 0.963301 1.201206
136 0.508391 -0.196713 -3.745356 -1.520113
235 -0.242459 -3.056990 1.918403 -0.578828
258 0.682841 0.326045 0.425384 -3.428254
322 1.179227 -3.184377 1.369891 -1.074833
544 -3.548824 1.553205 -2.186301 1.277104
635 -0.578093 0.193299 1.397822 3.366626
782 -0.207434 3.525865 0.283070 0.544635
803 -3.645860 0.255475 -0.549574 -1.907459
```

The parentheses around `data.abs() > 3`

are necessary in order to call the `any`

method on the result of the comparison operation.

Values can be set based on these criteria. Here is code to cap values outside the interval –3 to 3:

```
100]: data[data.abs() > 3] = np.sign(data) * 3
In [
101]: data.describe()
In [101]:
Out[0 1 2 3
1000.000000 1000.000000 1000.000000 1000.000000
count 0.050286 0.025567 -0.001399 -0.051765
mean 0.992920 1.004214 0.991414 0.995761
std min -3.000000 -3.000000 -3.000000 -3.000000
25% -0.599807 -0.612162 -0.687373 -0.747478
50% 0.047101 -0.013609 -0.022158 -0.088274
75% 0.756646 0.695298 0.699046 0.623331
max 2.653656 3.000000 2.735527 3.000000
```

The statement `np.sign(data)`

produces 1 and –1 values based on whether the values in `data`

are positive or negative:

```
102]: np.sign(data).head()
In [102]:
Out[0 1 2 3
0 -1.0 1.0 -1.0 1.0
1 1.0 -1.0 1.0 -1.0
2 1.0 1.0 1.0 -1.0
3 -1.0 -1.0 1.0 -1.0
4 -1.0 1.0 -1.0 -1.0
```

### Permutation and Random Sampling

Permuting (randomly reordering) a Series or the rows in a DataFrame is possible using the `numpy.random.permutation`

function. Calling `permutation`

with the length of the axis you want to permute produces an array of integers indicating the new ordering:

```
103]: df = pd.DataFrame(np.arange(5 * 7).reshape((5, 7)))
In [
104]: df
In [104]:
Out[0 1 2 3 4 5 6
0 0 1 2 3 4 5 6
1 7 8 9 10 11 12 13
2 14 15 16 17 18 19 20
3 21 22 23 24 25 26 27
4 28 29 30 31 32 33 34
105]: sampler = np.random.permutation(5)
In [
106]: sampler
In [106]: array([3, 1, 4, 2, 0]) Out[
```

That array can then be used in `iloc`

-based indexing or the equivalent `take`

function:

```
107]: df.take(sampler)
In [107]:
Out[0 1 2 3 4 5 6
3 21 22 23 24 25 26 27
1 7 8 9 10 11 12 13
4 28 29 30 31 32 33 34
2 14 15 16 17 18 19 20
0 0 1 2 3 4 5 6
108]: df.iloc[sampler]
In [108]:
Out[0 1 2 3 4 5 6
3 21 22 23 24 25 26 27
1 7 8 9 10 11 12 13
4 28 29 30 31 32 33 34
2 14 15 16 17 18 19 20
0 0 1 2 3 4 5 6
```

We could also select a permutation of the columns by invoking `take`

with `axis="columns"`

:

```
109]: column_sampler = np.random.permutation(7)
In [
110]: column_sampler
In [110]: array([4, 6, 3, 2, 1, 0, 5])
Out[
111]: df.take(column_sampler, axis="columns")
In [111]:
Out[4 6 3 2 1 0 5
0 4 6 3 2 1 0 5
1 11 13 10 9 8 7 12
2 18 20 17 16 15 14 19
3 25 27 24 23 22 21 26
4 32 34 31 30 29 28 33
```

To select a random subset without replacement (the same row cannot appear twice), you can use the `sample`

method on Series and DataFrame:

```
112]: df.sample(n=3)
In [112]:
Out[0 1 2 3 4 5 6
2 14 15 16 17 18 19 20
4 28 29 30 31 32 33 34
0 0 1 2 3 4 5 6
```

To generate a sample *with* replacement (to allow repeat choices), pass `replace=True`

to `sample`

:

```
113]: choices = pd.Series([5, 7, -1, 6, 4])
In [
114]: choices.sample(n=10, replace=True)
In [114]:
Out[2 -1
0 5
3 6
1 7
4 4
0 5
4 4
0 5
4 4
4 4
dtype: int64
```

### Computing Indicator/Dummy Variables

Another type of transformation for statistical modeling or machine learning applications is converting a categorical variable into a “dummy” or “indicator” matrix. If a column in a DataFrame has `k`

distinct values, you would derive a matrix or DataFrame with `k`

columns containing all 1s and 0s. pandas has a `pandas.get_dummies`

function for doing this, though you could also devise one yourself. Let’s consider an example DataFrame:

```
115]: df = pd.DataFrame({"key": ["b", "b", "a", "c", "a", "b"],
In ["data1": range(6)})
.....:
116]: df
In [116]:
Out[
key data10 b 0
1 b 1
2 a 2
3 c 3
4 a 4
5 b 5
117]: pd.get_dummies(df["key"])
In [117]:
Out[
a b c0 0 1 0
1 0 1 0
2 1 0 0
3 0 0 1
4 1 0 0
5 0 1 0
```

In some cases, you may want to add a prefix to the columns in the indicator DataFrame, which can then be merged with the other data. `pandas.get_dummies`

has a prefix argument for doing this:

```
118]: dummies = pd.get_dummies(df["key"], prefix="key")
In [
119]: df_with_dummy = df[["data1"]].join(dummies)
In [
120]: df_with_dummy
In [120]:
Out[
data1 key_a key_b key_c0 0 0 1 0
1 1 0 1 0
2 2 1 0 0
3 3 0 0 1
4 4 1 0 0
5 5 0 1 0
```

The `DataFrame.join`

method will be explained in more detail in the next chapter.

If a row in a DataFrame belongs to multiple categories, we have to use a different approach to create the dummy variables. Let’s look at the MovieLens 1M dataset, which is investigated in more detail in Ch 13: Data Analysis Examples:

```
121]: mnames = ["movie_id", "title", "genres"]
In [
122]: movies = pd.read_table("datasets/movielens/movies.dat", sep="::",
In [=None, names=mnames, engine="python")
.....: header
123]: movies[:10]
In [123]:
Out[
movie_id title genres0 1 Toy Story (1995) Animation|Children's|Comedy
1 2 Jumanji (1995) Adventure|Children's|Fantasy
2 3 Grumpier Old Men (1995) Comedy|Romance
3 4 Waiting to Exhale (1995) Comedy|Drama
4 5 Father of the Bride Part II (1995) Comedy
5 6 Heat (1995) Action|Crime|Thriller
6 7 Sabrina (1995) Comedy|Romance
7 8 Tom and Huck (1995) Adventure|Children's
8 9 Sudden Death (1995) Action
9 10 GoldenEye (1995) Action|Adventure|Thriller
```

pandas has implemented a special Series method `str.get_dummies`

(methods that start with `str.`

are discussed in more detail later in String Manipulation) that handles this scenario of multiple group membership encoded as a delimited string:

```
124]: dummies = movies["genres"].str.get_dummies("|")
In [
125]: dummies.iloc[:10, :6]
In [125]:
Out['s Comedy Crime
Action Adventure Animation Children0 0 0 1 1 1 0
1 0 1 0 1 0 0
2 0 0 0 0 1 0
3 0 0 0 0 1 0
4 0 0 0 0 1 0
5 1 0 0 0 0 1
6 0 0 0 0 1 0
7 0 1 0 1 0 0
8 1 0 0 0 0 0
9 1 1 0 0 0 0
```

Then, as before, you can combine this with `movies`

while adding a `"Genre_"`

to the column names in the `dummies`

DataFrame with the `add_prefix`

method:

```
126]: movies_windic = movies.join(dummies.add_prefix("Genre_"))
In [
127]: movies_windic.iloc[0]
In [127]:
Out[1
movie_id 1995)
title Toy Story (|Children's|Comedy
genres AnimationGenre_Action 0
Genre_Adventure 0
Genre_Animation 1
Genre_Children's 1
Genre_Comedy 1
Genre_Crime 0
Genre_Documentary 0
Genre_Drama 0
Genre_Fantasy 0
Genre_Film-Noir 0
Genre_Horror 0
Genre_Musical 0
Genre_Mystery 0
Genre_Romance 0
Genre_Sci-Fi 0
Genre_Thriller 0
Genre_War 0
Genre_Western 0
Name: 0, dtype: object
```

A useful recipe for statistical applications is to combine `pandas.get_dummies`

with a discretization function like `pandas.cut`

:

```
128]: np.random.seed(12345) # to make the example repeatable
In [
129]: values = np.random.uniform(size=10)
In [
130]: values
In [130]:
Out[0.9296, 0.3164, 0.1839, 0.2046, 0.5677, 0.5955, 0.9645, 0.6532,
array([0.7489, 0.6536])
131]: bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
In [
132]: pd.get_dummies(pd.cut(values, bins))
In [132]:
Out[0.0, 0.2] (0.2, 0.4] (0.4, 0.6] (0.6, 0.8] (0.8, 1.0]
(0 0 0 0 0 1
1 0 1 0 0 0
2 1 0 0 0 0
3 0 1 0 0 0
4 0 0 1 0 0
5 0 0 1 0 0
6 0 0 0 0 1
7 0 0 0 1 0
8 0 0 0 1 0
9 0 0 0 1 0
```

We will look again at `pandas.get_dummies`

later in Creating dummy variables for modeling.

## 7.3 Extension Data Types

pandas was originally built upon the capabilities present in NumPy, an array computing library used primarily for working with numerical data. Many pandas concepts, such as missing data, were implemented using what was available NumPy while trying to maximize compatibility between libraries that used NumPy and pandas together.

Building on NumPy led to a number of shortcomings, such as:

Missing data handling for some numerical data types, such as integers and booleans, was incomplete. As a result, when missing data would be introduced into such data, pandas would convert the data type to

`float64`

and use`np.nan`

to represent null values. This had compounding effects by introducing subtle issues into many pandas algorithms.Data sets with a lot of string data were computationally expensive and used a lot of memory.

Some data types, like time intervals, timedeltas, timestamps with time zones could not be supported efficiently without using computationally expensive arrays of Python objects.

More recently, pandas has developed an *extension type* system allowing for new data types to be added even if they are not supported natively by NumPy. These new data types could be treated as first class alongside data coming from NumPy arrays.

Let's look at an example where we create a Series of integers with a missing value:

```
133]: s = pd.Series([1, 2, 3, None])
In [
134]: s
In [134]:
Out[0 1.0
1 2.0
2 3.0
3 NaN
dtype: float64
135]: s.dtype
In [135]: dtype('float64') Out[
```

Mainly for backward compatibility reasons, Series uses the legacy behavior of using a `float64`

data type and `np.nan`

for the missing value. We could create this Series instead using `pandas.Int64Dtype`

:

```
136]: s = pd.Series([1, 2, 3, None], dtype=pd.Int64Dtype())
In [
137]: s
In [137]:
Out[0 1
1 2
2 3
3 <NA>
dtype: Int64
138]: s.isna()
In [138]:
Out[0 False
1 False
2 False
3 True
bool
dtype:
139]: s.dtype
In [139]: Int64Dtype() Out[
```

The output `<NA>`

indicates that a value is missing for an extension type array. This uses the special `pandas.NA`

sentinel value:

```
140]: s[3]
In [140]: <NA>
Out[
141]: s[3] is pd.NA
In [141]: True Out[
```

We could also have used the shorthand `"Int64"`

instead of `pd.Int64Dtype()`

to specify the type. The capitalization is necessary otherwise you will be a NumPy-based non-extension type:

`142]: s = pd.Series([1, 2, 3, None], dtype="Int64") In [`

pandas also has an extension type specialized for string data that does not use NumPy object arrays (it requires the pyarrow library, which you may need to install separately):

```
143]: s = pd.Series(['one', 'two', None, 'three'], dtype=pd.StringDtype())
In [
144]: s
In [144]:
Out[0 one
1 two
2 <NA>
3 three
dtype: string
```

These string arrays generally use much less memory and are frequently computationally more efficient for doing operations on large data sets.

Another important extension type is Categorical, which we discuss in more detail in Categorical Data. A reasonably complete list of extension types available as of this writing is in table_title.

Extension types can be passed to the Series `astype`

method, allowing you to convert easily as part of your data cleaning process:

```
145]: df = pd.DataFrame({"A": [1, 2, None, 4],
In ["B": ["one", "two", "three", None],
.....: "C": [False, None, False, True]})
.....:
146]: df
In [146]:
Out[
A B C0 1.0 one False
1 2.0 two None
2 NaN three False
3 4.0 None True
147]: df["A"] = df["A"].astype("Int64")
In [
148]: df["B"] = df["B"].astype("string")
In [
149]: df["C"] = df["C"].astype("boolean")
In [
150]: df
In [150]:
Out[
A B C0 1 one False
1 2 two <NA>
2 <NA> three False
3 4 <NA> True
```

Extension Type | Description |
---|---|

`BooleanDtype` |
Nullable boolean data, use `"boolean"` when passing as string |

`CategoricalDtype` |
Categorical data type, use `"category"` when passing as string |

`DatetimeTZDtype` |
Datetime with time zone |

`Float32Dtype` |
32-bit nullable floating point, use `"Float32"` when passing as string |

`Float64Dtype` |
64-bit nullable floating point, use `"Float64"` when passing as string |

`Int8Dtype` |
8-bit nullable signed integer, use `"Int8"` when passing as string |

`Int16Dtype` |
16-bit nullable signed integer, use `"Int16"` when passing as string |

`Int32Dtype` |
32-bit nullable signed integer, use `"Int32"` when passing as string |

`Int64Dtype` |
64-bit nullable signed integer, use `"Int64"` when passing as string |

`UInt8Dtype` |
8-bit nullable unsigned integer, use `"UInt8"` when passing as string |

`UInt16Dtype` |
16-bit nullable unsigned integer, use `"UInt16"` when passing as string |

`UInt32Dtype` |
32-bit nullable unsigned integer, use `"UInt32"` when passing as string |

`UInt64Dtype` |
64-bit nullable unsigned integer, use `"UInt64"` when passing as string |

## 7.4 String Manipulation

Python has long been a popular raw data manipulation language in part due to its ease of use for string and text processing. Most text operations are made simple with the string object’s built-in methods. For more complex pattern matching and text manipulations, regular expressions may be needed. pandas adds to the mix by enabling you to apply string and regular expressions concisely on whole arrays of data, additionally handling the annoyance of missing data.

### Python Built-In String Object Methods

In many string munging and scripting applications, built-in string methods are sufficient. As an example, a comma-separated string can be broken into pieces with `split`

:

```
151]: val = "a,b, guido"
In [
152]: val.split(",")
In [152]: ['a', 'b', ' guido'] Out[
```

`split`

is often combined with `strip`

to trim whitespace (including line breaks):

```
153]: pieces = [x.strip() for x in val.split(",")]
In [
154]: pieces
In [154]: ['a', 'b', 'guido'] Out[
```

These substrings could be concatenated together with a two-colon delimiter using addition:

```
155]: first, second, third = pieces
In [
156]: first + "::" + second + "::" + third
In [156]: 'a::b::guido' Out[
```

But this isn’t a practical generic method. A faster and more Pythonic way is to pass a list or tuple to the `join`

method on the string `"::"`

:

```
157]: "::".join(pieces)
In [157]: 'a::b::guido' Out[
```

Other methods are concerned with locating substrings. Using Python’s `in`

keyword is the best way to detect a substring, though `index`

and `find`

can also be used:

```
158]: "guido" in val
In [158]: True
Out[
159]: val.index(",")
In [159]: 1
Out[
160]: val.find(":")
In [160]: -1 Out[
```

Note the difference between `find`

and `index`

is that `index`

raises an exception if the string isn’t found (versus returning –1):

```
161]: val.index(":")
In [---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-161-bea4c4c30248> in <module>
----> 1 val.index(":")
ValueError: substring not found
```

Relatedly, `count`

returns the number of occurrences of a particular substring:

```
162]: val.count(",")
In [162]: 2 Out[
```

`replace`

will substitute occurrences of one pattern for another. It is commonly used to delete patterns, too, by passing an empty string:

```
163]: val.replace(",", "::")
In [163]: 'a::b:: guido'
Out[
164]: val.replace(",", "")
In [164]: 'ab guido' Out[
```

See Table 7.4 for a listing of some of Python's string methods.

Regular expressions can also be used with many of these operations, as you’ll see.

Method | Description |
---|---|

`count` |
Return the number of non-overlapping occurrences of substring in the string |

`endswith` |
Returns `True` if string ends with suffix |

`startswith` |
Returns `True` if string starts with prefix |

`join` |
Use string as delimiter for concatenating a sequence of other strings |

`index` |
Return starting index of the first occurrence of passed substring if found in the string; raises `ValueError` if not found |

`find` |
Return position of first character of first occurrence of substring in the string; like `index` , but returns –1 if not found |

`rfind` |
Return position of first character of last occurrence of substring in the string; returns –1 if not found |

`replace` |
Replace occurrences of string with another string |

`strip, rstrip, lstrip` |
Trim whitespace, including newlines on both sides, on the right side, or on the left side, respectively |

`split` |
Break string into list of substrings using passed delimiter |

`lower` |
Convert alphabet characters to lowercase |

`upper` |
Convert alphabet characters to uppercase |

`casefold` |
Convert characters to lowercase, and convert any region-specific variable character combinations to a common comparable form |

`ljust, rjust` |
Left justify or right justify, respectively; pad opposite side of string with spaces (or some other fill character) to return a string with a minimum width |

### Regular Expressions

*Regular expressions* provide a flexible way to search or match (often more complex) string patterns in text. A single expression, commonly called a *regex*, is a string formed according to the regular expression language. Python’s built-in `re`

module is responsible for applying regular expressions to strings; I’ll give a number of examples of its use here.

The `re`

module functions fall into three categories: pattern matching, substitution, and splitting. Naturally these are all related; a regex describes a pattern to locate in the text, which can then be used for many purposes. Let’s look at a simple example: suppose we wanted to split a string with a variable number of whitespace characters (tabs, spaces, and newlines). The regex describing one or more whitespace characters is `\s+`

:

```
165]: import re
In [
166]: text = "foo bar\t baz \tqux"
In [
167]: re.split(r"\s+", text)
In [167]: ['foo', 'bar', 'baz', 'qux'] Out[
```

When you call `re.split(r"\s+", text)`

, the regular expression is first *compiled*, and then its `split`

method is called on the passed text. You can compile the regex yourself with `re.compile`

, forming a reusable regex object:

```
168]: regex = re.compile(r"\s+")
In [
169]: regex.split(text)
In [169]: ['foo', 'bar', 'baz', 'qux'] Out[
```

If, instead, you wanted to get a list of all patterns matching the regex, you can use the `findall`

method:

```
170]: regex.findall(text)
In [170]: [' ', '\t ', ' \t'] Out[
```

Creating a regex object with `re.compile`

is highly recommended if you intend to apply the same expression to many strings; doing so will save CPU cycles.

`match`

and `search`

are closely related to `findall`

. While `findall`

returns all matches in a string, `search`

returns only the first match. More rigidly, `match`

*only* matches at the beginning of the string. As a less trivial example, let’s consider a block of text and a regular expression capable of identifying most email addresses:

```
= """Dave dave@google.com
text Steve steve@gmail.com
Rob rob@gmail.com
Ryan ryan@yahoo.com"""
= r"[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}"
pattern
# re.IGNORECASE makes the regex case-insensitive
= re.compile(pattern, flags=re.IGNORECASE) regex
```

Using `findall`

on the text produces a list of the email addresses:

```
172]: regex.findall(text)
In [172]:
Out['dave@google.com',
['steve@gmail.com',
'rob@gmail.com',
'ryan@yahoo.com']
```

`search`

returns a special match object for the first email address in the text. For the preceding regex, the match object can only tell us the start and end position of the pattern in the string:

```
173]: m = regex.search(text)
In [
174]: m
In [174]: <re.Match object; span=(5, 20), match='dave@google.com'>
Out[
175]: text[m.start():m.end()]
In [175]: 'dave@google.com' Out[
```

`regex.match`

returns `None`

, as it only will match if the pattern occurs at the start of the string:

```
176]: print(regex.match(text))
In [None
```

Relatedly, `sub`

will return a new string with occurrences of the pattern replaced by the a new string:

```
177]: print(regex.sub("REDACTED", text))
In [
Dave REDACTED
Steve REDACTED
Rob REDACTED Ryan REDACTED
```

Suppose you wanted to find email addresses and simultaneously segment each address into its three components: username, domain name, and domain suffix. To do this, put parentheses around the parts of the pattern to segment:

```
178]: pattern = r"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})"
In [
179]: regex = re.compile(pattern, flags=re.IGNORECASE) In [
```

A match object produced by this modified regex returns a tuple of the pattern components with its `groups`

method:

```
180]: m = regex.match("wesm@bright.net")
In [
181]: m.groups()
In [181]: ('wesm', 'bright', 'net') Out[
```

`findall`

returns a list of tuples when the pattern has groups:

```
182]: regex.findall(text)
In [182]:
Out['dave', 'google', 'com'),
[('steve', 'gmail', 'com'),
('rob', 'gmail', 'com'),
('ryan', 'yahoo', 'com')] (
```

`sub`

also has access to groups in each match using special symbols like `\1`

and `\2`

. The symbol `\1`

corresponds to the first matched group, `\2`

corresponds to the second, and so forth:

```
183]: print(regex.sub(r"Username: \1, Domain: \2, Suffix: \3", text))
In [
Dave Username: dave, Domain: google, Suffix: com
Steve Username: steve, Domain: gmail, Suffix: com
Rob Username: rob, Domain: gmail, Suffix: com Ryan Username: ryan, Domain: yahoo, Suffix: com
```

There is much more to regular expressions in Python, most of which is outside the book’s scope. Table 7.5 provides a brief summary.

Method | Description |
---|---|

`findall` |
Return all non-overlapping matching patterns in a string as a list |

`finditer` |
Like `findall` , but returns an iterator |

`match` |
Match pattern at start of string and optionally segment pattern components into groups; if the pattern matches, returns a match object, and otherwise `None` |

`search` |
Scan string for match to pattern; returning a match object if so; unlike `match` , the match can be anywhere in the string as opposed to only at the beginning |

`split` |
Break string into pieces at each occurrence of pattern |

`sub, subn` |
Replace all (`sub` ) or first `n` occurrences (`subn` ) of pattern in string with replacement expression; use symbols `\1, \2, ...` to refer to match group elements in the replacement string |

### String Functions in pandas

Cleaning up a messy dataset for analysis often requires a lot of string manipulation. To complicate matters, a column containing strings will sometimes have missing data:

```
184]: data = {"Dave": "dave@google.com", "Steve": "steve@gmail.com",
In ["Rob": "rob@gmail.com", "Wes": np.nan}
.....:
185]: data = pd.Series(data)
In [
186]: data
In [186]:
Out[@google.com
Dave dave@gmail.com
Steve steve@gmail.com
Rob rob
Wes NaNobject
dtype:
187]: data.isna()
In [187]:
Out[False
Dave False
Steve False
Rob True
Wes bool dtype:
```

String and regular expression methods can be applied (passing a `lambda`

or other function) to each value using `data.map`

, but it will fail on the NA (null) values. To cope with this, Series has array-oriented methods for string operations that skip over and propagate NA values. These are accessed through Series’s `str`

attribute; for example, we could check whether each email address has `"gmail"`

in it with `str.contains`

:

```
188]: data.str.contains("gmail")
In [188]:
Out[False
Dave True
Steve True
Rob
Wes NaNobject dtype:
```

Note that the result of this operation has an `object`

dtype. pandas has *extension types* which provide for specialized treatment of strings, integers, and boolean data which until recently have had some rough edges when working with missing data:

```
189]: data_as_string_ext = data.astype('string')
In [
190]: data_as_string_ext
In [190]:
Out[@google.com
Dave dave@gmail.com
Steve steve@gmail.com
Rob rob<NA>
Wes
dtype: string
191]: data_as_string_ext.str.contains("gmail")
In [191]:
Out[False
Dave True
Steve True
Rob <NA>
Wes dtype: boolean
```

Extension types are discussed in more detail in Extension Data Types.

Regular expressions can be used, too, along with any `re`

options like `IGNORECASE`

:

```
192]: pattern = r"([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})"
In [
193]: data.str.findall(pattern, flags=re.IGNORECASE)
In [193]:
Out[
Dave [(dave, google, com)]
Steve [(steve, gmail, com)]
Rob [(rob, gmail, com)]
Wes NaNobject dtype:
```

There are a couple of ways to do vectorized element retrieval. Either use `str.get`

or index into the `str`

attribute:

```
194]: matches = data.str.findall(pattern, flags=re.IGNORECASE).str[0]
In [
195]: matches
In [195]:
Out[
Dave (dave, google, com)
Steve (steve, gmail, com)
Rob (rob, gmail, com)
Wes NaNobject
dtype:
196]: matches.str.get(1)
In [196]:
Out[
Dave google
Steve gmail
Rob gmail
Wes NaNobject dtype:
```

You can similarly slice strings using this syntax:

```
197]: data.str[:5]
In [197]:
Out[@
Dave dave
Steve steve@g
Rob rob
Wes NaNobject dtype:
```

The `str.extract`

method will return the captured groups of a regular expression as a DataFrame:

```
198]: data.str.extract(pattern, flags=re.IGNORECASE)
In [198]:
Out[0 1 2
Dave dave google com
Steve steve gmail com
Rob rob gmail com Wes NaN NaN NaN
```

See Table 7.6 for more pandas string methods.

Method | Description |
---|---|

`cat` |
Concatenate strings element-wise with optional delimiter |

`contains` |
Return boolean array if each string contains pattern/regex |

`count` |
Count occurrences of pattern |

`extract` |
Use a regular expression with groups to extract one or more strings from a Series of strings; the result will be a DataFrame with one column per group |

`endswith` |
Equivalent to `x.endswith(pattern)` for each element |

`startswith` |
Equivalent to `x.startswith(pattern)` for each element |

`findall` |
Compute list of all occurrences of pattern/regex for each string |

`get` |
Index into each element (retrieve i-th element) |

`isalnum` |
Equivalent to built-in `str.alnum` |

`isalpha` |
Equivalent to built-in `str.isalpha` |

`isdecimal` |
Equivalent to built-in `str.isdecimal` |

`isdigit` |
Equivalent to built-in `str.isdigit` |

`islower` |
Equivalent to built-in `str.islower` |

`isnumeric` |
Equivalent to built-in `str.isnumeric` |

`isupper` |
Equivalent to built-in `str.isupper` |

`join` |
Join strings in each element of the Series with passed separator |

`len` |
Compute length of each string |

`lower, upper` |
Convert cases; equivalent to `x.lower()` or `x.upper()` for each element |

`match` |
Use `re.match` with the passed regular expression on each element, returning `True` or `False` whether it matches. |

`pad` |
Add whitespace to left, right, or both sides of strings |

`center` |
Equivalent to `pad(side="both")` |

`repeat` |
Duplicate values (e.g., `s.str.repeat(3)` is equivalent to `x * 3` for each string) |

`replace` |
Replace occurrences of pattern/regex with some other string |

`slice` |
Slice each string in the Series |

`split` |
Split strings on delimiter or regular expression |

`strip` |
Trim whitespace from both sides, including newlines |

`rstrip` |
Trim whitespace on right side |

`lstrip` |
Trim whitespace on left side |

## 7.5 Categorical Data

This section introduces the pandas `Categorical`

type. I will show how you can achieve better performance and memory use in some pandas operations by using it. I also introduce some tools that may help with using categorical data in statistics and machine learning applications.

### Background and Motivation

Frequently, a column in a table may contain repeated instances of a smaller set of distinct values. We have already seen functions like `unique`

and `value_counts`

, which enable us to extract the distinct values from an array and compute their frequencies, respectively:

```
199]: values = pd.Series(['apple', 'orange', 'apple',
In ['apple'] * 2)
.....:
200]: values
In [200]:
Out[0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
object
dtype:
201]: pd.unique(values)
In [201]: array(['apple', 'orange'], dtype=object)
Out[
202]: pd.value_counts(values)
In [202]:
Out[6
apple 2
orange dtype: int64
```

Many data systems (for data warehousing, statistical computing, or other uses) have developed specialized approaches for representing data with repeated values for more efficient storage and computation. In data warehousing, a best practice is to use so-called *dimension tables* containing the distinct values and storing the primary observations as integer keys referencing the dimension table:

```
203]: values = pd.Series([0, 1, 0, 0] * 2)
In [
204]: dim = pd.Series(['apple', 'orange'])
In [
205]: values
In [205]:
Out[0 0
1 1
2 0
3 0
4 0
5 1
6 0
7 0
dtype: int64
206]: dim
In [206]:
Out[0 apple
1 orange
object dtype:
```

We can use the `take`

method to restore the original Series of strings:

```
207]: dim.take(values)
In [207]:
Out[0 apple
1 orange
0 apple
0 apple
0 apple
1 orange
0 apple
0 apple
object dtype:
```

This representation as integers is called the *categorical* or *dictionary-encoded* representation. The array of distinct values can be called the *categories*, *dictionary*, or *levels* of the data. In this book we will use the terms *categorical* and *categories*. The integer values that reference the categories are called the *category codes* or simply *codes*.

The categorical representation can yield significant performance improvements when you are doing analytics. You can also perform transformations on the categories while leaving the codes unmodified. Some example transformations that can be made at relatively low cost are:

Renaming categories

Appending a new category without changing the order or position of the existing categories

### Categorical Extension Type in pandas

pandas has a special `Categorical`

extension type for holding data that uses the integer-based categorical representation or *encoding*. This is a popular data compression technique for data with many occurrences of similar values and can provide significantly faster performance with lower memory use, especially for string data.

Let's consider the example Series from before:

```
208]: fruits = ['apple', 'orange', 'apple', 'apple'] * 2
In [
209]: N = len(fruits)
In [
210]: rng = np.random.default_rng(seed=12345)
In [
211]: df = pd.DataFrame({'fruit': fruits,
In ['basket_id': np.arange(N),
.....: 'count': rng.integers(3, 15, size=N),
.....: 'weight': rng.uniform(0, 4, size=N)},
.....: =['basket_id', 'fruit', 'count', 'weight'])
.....: columns
212]: df
In [212]:
Out[
basket_id fruit count weight0 0 apple 11 1.564438
1 1 orange 5 1.331256
2 2 apple 12 2.393235
3 3 apple 6 0.746937
4 4 apple 5 2.691024
5 5 orange 12 3.767211
6 6 apple 10 0.992983
7 7 apple 11 3.795525
```

Here, `df['fruit']`

is an array of Python string objects. We can convert it to categorical by calling:

```
213]: fruit_cat = df['fruit'].astype('category')
In [
214]: fruit_cat
In [214]:
Out[0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
Name: fruit, dtype: category2, object): ['apple', 'orange'] Categories (
```

The values for `fruit_cat`

are now an instance of `pandas.Categorical`

, which you can access see via the `.array`

attribute:

```
215]: c = fruit_cat.array
In [
216]: type(c)
In [216]: pandas.core.arrays.categorical.Categorical Out[
```

The `Categorical`

object has `categories`

and `codes`

attributes:

```
217]: c.categories
In [217]: Index(['apple', 'orange'], dtype='object')
Out[
218]: c.codes
In [218]: array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int8) Out[
```

These can be accessed more easily using the `cat`

accessor, which will be explained soon in Categorical Methods.

A useful trick to get a mapping between codes and categories is:

```
219]: dict(enumerate(c.categories))
In [219]: {0: 'apple', 1: 'orange'} Out[
```

You can convert a DataFrame column to categorical by assigning the converted result:

```
220]: df['fruit'] = df['fruit'].astype('category')
In [
221]: df["fruit"]
In [221]:
Out[0 apple
1 orange
2 apple
3 apple
4 apple
5 orange
6 apple
7 apple
Name: fruit, dtype: category2, object): ['apple', 'orange'] Categories (
```

You can also create `pandas.Categorical`

directly from other types of Python sequences:

```
222]: my_categories = pd.Categorical(['foo', 'bar', 'baz', 'foo', 'bar'])
In [
223]: my_categories
In [223]:
Out['foo', 'bar', 'baz', 'foo', 'bar']
[3, object): ['bar', 'baz', 'foo'] Categories (
```

If you have obtained categorical encoded data from another source, you can use the alternative `from_codes`

constructor:

```
224]: categories = ['foo', 'bar', 'baz']
In [
225]: codes = [0, 1, 2, 0, 0, 1]
In [
226]: my_cats_2 = pd.Categorical.from_codes(codes, categories)
In [
227]: my_cats_2
In [227]:
Out['foo', 'bar', 'baz', 'foo', 'foo', 'bar']
[3, object): ['foo', 'bar', 'baz'] Categories (
```

Unless explicitly specified, categorical conversions assume no specific ordering of the categories. So the `categories`

array may be in a different order depending on the ordering of the input data. When using `from_codes`

or any of the other constructors, you can indicate that the categories have a meaningful ordering:

```
228]: ordered_cat = pd.Categorical.from_codes(codes, categories,
In [=True)
.....: ordered
229]: ordered_cat
In [229]:
Out['foo', 'bar', 'baz', 'foo', 'foo', 'bar']
[3, object): ['foo' < 'bar' < 'baz'] Categories (
```

The output `[foo < bar < baz]`

indicates that `'foo'`

precedes `'bar'`

in the ordering, and so on. An unordered categorical instance can be made ordered with `as_ordered`

:

```
230]: my_cats_2.as_ordered()
In [230]:
Out['foo', 'bar', 'baz', 'foo', 'foo', 'bar']
[3, object): ['foo' < 'bar' < 'baz'] Categories (
```

As a last note, categorical data need not be strings, even though I have only showed string examples. A categorical array can consist of any immutable value types.

### Computations with Categoricals

Using `Categorical`

in pandas compared with the non-encoded version (like an array of strings) generally behaves the same way. Some parts of pandas, like the `groupby`

function, perform better when working with categoricals. There are also some functions that can utilize the `ordered`

flag.

Let's consider some random numeric data, and use the `pandas.qcut`

binning function. This returns `pandas.Categorical`

; we used `pandas.cut`

earlier in the book but glossed over the details of how categoricals work:

```
231]: rng = np.random.default_rng(seed=12345)
In [
232]: draws = rng.standard_normal(1000)
In [
233]: draws[:5]
In [233]: array([-1.4238, 1.2637, -0.8707, -0.2592, -0.0753]) Out[
```

Let's compute a quartile binning of this data and extract some statistics:

```
234]: bins = pd.qcut(draws, 4)
In [
235]: bins
In [235]:
Out[-3.121, -0.675], (0.687, 3.211], (-3.121, -0.675], (-0.675, 0.0134], (-0.675, 0
[(.0134], ..., (0.0134, 0.687], (0.0134, 0.687], (-0.675, 0.0134], (0.0134, 0.687],
-0.675, 0.0134]]
(1000
Length: 4, interval[float64, right]): [(-3.121, -0.675] < (-0.675, 0.0134] <
Categories (0.0134, 0.687] <
(0.687, 3.211]] (
```

While useful, the exact sample quartiles may be less useful for producing a report than quartile names. We can achieve this with the `labels`

argument to `qcut`

:

```
236]: bins = pd.qcut(draws, 4, labels=['Q1', 'Q2', 'Q3', 'Q4'])
In [
237]: bins
In [237]:
Out['Q1', 'Q4', 'Q1', 'Q2', 'Q2', ..., 'Q3', 'Q3', 'Q2', 'Q3', 'Q2']
[1000
Length: 4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']
Categories (
238]: bins.codes[:10]
In [238]: array([0, 3, 0, 1, 1, 0, 0, 2, 2, 0], dtype=int8) Out[
```

The labeled `bins`

categorical does not contain information about the bin edges in the data, so we can use `groupby`

to extract some summary statistics:

```
239]: bins = pd.Series(bins, name='quartile')
In [
240]: results = (pd.Series(draws)
In [
.....: .groupby(bins)'count', 'min', 'max'])
.....: .agg([
.....: .reset_index())
241]: results
In [241]:
Out[min max
quartile count 0 Q1 250 -3.119609 -0.678494
1 Q2 250 -0.673305 0.008009
2 Q3 250 0.018753 0.686183
3 Q4 250 0.688282 3.211418
```

The `'quartile'`

column in the result retains the original categorical information, including ordering, from `bins`

:

```
242]: results['quartile']
In [242]:
Out[0 Q1
1 Q2
2 Q3
3 Q4
Name: quartile, dtype: category4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4'] Categories (
```

#### Better performance with Categoricals

At the beginning of the section, I said that categorical types can improve performance and memory use, so let's look at some examples. Consider some Series with 10 million elements and a small number of distinct categories:

```
243]: N = 10_000_000
In [
244]: labels = pd.Series(['foo', 'bar', 'baz', 'qux'] * (N // 4)) In [
```

Now we convert `labels`

to categorical:

`245]: categories = labels.astype('category') In [`

Now we note that `labels`

uses significantly more memory than `categories`

:

```
246]: labels.memory_usage(deep=True)
In [246]: 600000128
Out[
247]: categories.memory_usage(deep=True)
In [247]: 10000540 Out[
```

The conversion to category is not free, of course, but it is a one-time cost:

```
248]: %time _ = labels.astype('category')
In [264 ms, sys: 34.2 ms, total: 298 ms
CPU times: user 297 ms Wall time:
```

GroupBy operations can be significantly faster with categoricals because the underlying algorithms use the integer-based codes array instead of an array of strings. Here we compare the performance of `value_counts()`

, which internally uses the GroupBy machinery:

```
249]: %timeit labels.value_counts()
In [236 ms +- 1.69 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
250]: %timeit categories.value_counts()
In [30.8 ms +- 457 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
```

### Categorical Methods

Series containing categorical data have several special methods similar to the `Series.str`

specialized string methods. This also provides convenient access to the categories and codes. Consider the Series:

```
251]: s = pd.Series(['a', 'b', 'c', 'd'] * 2)
In [
252]: cat_s = s.astype('category')
In [
253]: cat_s
In [253]:
Out[0 a
1 b
2 c
3 d
4 a
5 b
6 c
7 d
dtype: category4, object): ['a', 'b', 'c', 'd'] Categories (
```

The special *accessor* attribute `cat`

provides access to categorical methods:

```
254]: cat_s.cat.codes
In [254]:
Out[0 0
1 1
2 2
3 3
4 0
5 1
6 2
7 3
dtype: int8
255]: cat_s.cat.categories
In [255]: Index(['a', 'b', 'c', 'd'], dtype='object') Out[
```

Suppose that we know the actual set of categories for this data extends beyond the four values observed in the data. We can use the `set_categories`

method to change them:

```
256]: actual_categories = ['a', 'b', 'c', 'd', 'e']
In [
257]: cat_s2 = cat_s.cat.set_categories(actual_categories)
In [
258]: cat_s2
In [258]:
Out[0 a
1 b
2 c
3 d
4 a
5 b
6 c
7 d
dtype: category5, object): ['a', 'b', 'c', 'd', 'e'] Categories (
```

While it appears that the data is unchanged, the new categories will be reflected in operations that use them. For example, `value_counts`

respects the categories, if present:

```
259]: cat_s.value_counts()
In [259]:
Out[2
a 2
b 2
c 2
d
dtype: int64
260]: cat_s2.value_counts()
In [260]:
Out[2
a 2
b 2
c 2
d 0
e dtype: int64
```

In large datasets, categoricals are often used as a convenient tool for memory savings and better performance. After you filter a large DataFrame or Series, many of the categories may not appear in the data. To help with this, we can use the `remove_unused_categories`

method to trim unobserved categories:

```
261]: cat_s3 = cat_s[cat_s.isin(['a', 'b'])]
In [
262]: cat_s3
In [262]:
Out[0 a
1 b
4 a
5 b
dtype: category4, object): ['a', 'b', 'c', 'd']
Categories (
263]: cat_s3.cat.remove_unused_categories()
In [263]:
Out[0 a
1 b
4 a
5 b
dtype: category2, object): ['a', 'b'] Categories (
```

See Table 7.7 for a listing of available categorical methods.

Method | Description |
---|---|

`add_categories` |
Append new (unused) categories at end of existing categories |

`as_ordered` |
Make categories ordered |

`as_unordered` |
Make categories unordered |

`remove_categories` |
Remove categories, setting any removed values to null |

`remove_unused_categories` |
Remove any category values which do not appear in the data |

`rename_categories` |
Replace categories with indicated set of new category names; cannot change the number of categories |

`reorder_categories` |
Behaves like `rename_categories` , but can also change the result to have ordered categories |

`set_categories` |
Replace the categories with the indicated set of new categories; can add or remove categories |

#### Creating dummy variables for modeling

When you're using statistics or machine learning tools, you'll often transform categorical data into *dummy variables*, also known as *one-hot* encoding. This involves creating a DataFrame with a column for each distinct category; these columns contain 1s for occurrences of a given category and 0 otherwise.

Consider the previous example:

`264]: cat_s = pd.Series(['a', 'b', 'c', 'd'] * 2, dtype='category') In [`

As mentioned previously in Data Cleaning and Preparation, the `pandas.get_dummies`

function converts this one-dimensional categorical data into a DataFrame containing the dummyvariable:

```
265]: pd.get_dummies(cat_s)
In [265]:
Out[
a b c d0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1
4 1 0 0 0
5 0 1 0 0
6 0 0 1 0
7 0 0 0 1
```

## 7.6 Conclusion

Effective data preparation can significantly improve productivity by enabling you to spend more time analyzing data and less time getting it ready for analysis. We have explored a number of tools in this chapter, but the coverage here is by no means comprehensive. In the next chapter, we will explore pandas's joining and grouping functionality.