categorical.rst

pandas

python

import numpy as np import pandas as pd np.random.seed(123456) np.set_printoptions(precision=4, suppress=True) pd.options.display.max_rows = 15

Categorical Data

This is an introduction to pandas categorical data type, including a short comparison with R's factor.

Categoricals are a pandas data type, which correspond to categorical variables in statistics: a variable, which can take on only a limited, and usually fixed, number of possible values (categories; levels in R). Examples are gender, social class, blood types, country affiliations, observation time or ratings via Likert scales.

In contrast to statistical categorical variables, categorical data might have an order (e.g. 'strongly agree' vs 'agree' or 'first observation' vs. 'second observation'), but numerical operations (additions, divisions, ...) are not possible.

All values of categorical data are either in categories or np.nan. Order is defined by the order of categories, not lexical order of the values. Internally, the data structure consists of a categories array and an integer array of codes which point to the real value in the categories array.

The categorical data type is useful in the following cases:

• A string variable consisting of only a few different values. Converting such a string variable to a categorical variable will save some memory, see here <categorical.memory>.
• The lexical order of a variable is not the same as the logical order ("one", "two", "three"). By converting to a categorical and specifying an order on the categories, sorting and min/max will use the logical order instead of the lexical order, see here <categorical.sort>.
• As a signal to other python libraries that this column should be treated as a categorical variable (e.g. to use suitable statistical methods or plot types).

See also the API docs on categoricals<api.categorical>.

Object Creation

Categorical Series or columns in a DataFrame can be created in several ways:

By specifying dtype="category" when constructing a `Series`:

python

s = pd.Series(["a","b","c","a"], dtype="category") s

By converting an existing Series or column to a category dtype:

python

df = pd.DataFrame({"A":["a","b","c","a"]}) df["B"] = df["A"].astype('category') df

By using some special functions:

python

df = pd.DataFrame({'value': np.random.randint(0, 100, 20)}) labels = [ "{0} - {1}".format(i, i + 9) for i in range(0, 100, 10) ]

df['group'] = pd.cut(df.value, range(0, 105, 10), right=False, labels=labels) df.head(10)

See documentation <reshaping.tile.cut> for ~pandas.cut.

By passing a pandas.Categorical object to a Series or assigning it to a DataFrame.

python

raw_cat = pd.Categorical(["a","b","c","a"], categories=["b","c","d"],

ordered=False)

s = pd.Series(raw_cat) s df = pd.DataFrame({"A":["a","b","c","a"]}) df["B"] = raw_cat df

Anywhere above we passed a keyword dtype='category', we used the default behavior of

1. categories are inferred from the data
2. categories are unordered.

To control those behaviors, instead of passing 'category', use an instance of ~pandas.api.types.CategoricalDtype.

python

from pandas.api.types import CategoricalDtype

s = pd.Series(["a", "b", "c", "a"]) cat_type = CategoricalDtype(categories=["b", "c", "d"], ordered=True) s_cat = s.astype(cat_type) s_cat

Categorical data has a specific category dtype <basics.dtypes>:

python

df.dtypes

Note

In contrast to R's factor function, categorical data is not converting input values to strings and categories will end up the same data type as the original values.

Note

In contrast to R's factor function, there is currently no way to assign/change labels at creation time. Use categories to change the categories after creation time.

To get back to the original Series or numpy array, use Series.astype(original_dtype) or np.asarray(categorical):

python

s = pd.Series(["a","b","c","a"]) s s2 = s.astype('category') s2 s2.astype(str) np.asarray(s2)

If you have already codes and categories, you can use the ~pandas.Categorical.from_codes constructor to save the factorize step during normal constructor mode:

python

splitter = np.random.choice([0,1], 5, p=[0.5,0.5]) s = pd.Series(pd.Categorical.from_codes(splitter, categories=["train", "test"]))

CategoricalDtype

0.21.0

A categorical's type is fully described by

1. categories: a sequence of unique values and no missing values
2. ordered: a boolean

This information can be stored in a ~pandas.api.types.CategoricalDtype. The categories argument is optional, which implies that the actual categories should be inferred from whatever is present in the data when the pandas.Categorical is created. The categories are assumed to be unordered by default.

python

from pandas.api.types import CategoricalDtype

CategoricalDtype(['a', 'b', 'c']) CategoricalDtype(['a', 'b', 'c'], ordered=True) CategoricalDtype()

A ~pandas.api.types.CategoricalDtype can be used in any place pandas expects a dtype. For example pandas.read_csv, pandas.DataFrame.astype, or in the Series constructor.

Note

As a convenience, you can use the string 'category' in place of a ~pandas.api.types.CategoricalDtype when you want the default behavior of the categories being unordered, and equal to the set values present in the array. In other words, dtype='category' is equivalent to dtype=CategoricalDtype().

Equality Semantics

Two instances of ~pandas.api.types.CategoricalDtype compare equal whenever they have the same categories and orderedness. When comparing two unordered categoricals, the order of the categories is not considered

python

c1 = CategoricalDtype(['a', 'b', 'c'], ordered=False)

# Equal, since order is not considered when ordered=False c1 == CategoricalDtype(['b', 'c', 'a'], ordered=False)

# Unequal, since the second CategoricalDtype is ordered c1 == CategoricalDtype(['a', 'b', 'c'], ordered=True)

All instances of CategoricalDtype compare equal to the string 'category'

python

c1 == 'category'

Warning

Since dtype='category' is essentially CategoricalDtype(None, False), and since all instances CategoricalDtype compare equal to 'category', all instances of CategoricalDtype compare equal to a CategoricalDtype(None, False), regardless of categories or ordered.

Description

Using .describe() on categorical data will produce similar output to a Series or DataFrame of type string.

python

cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"]) df = pd.DataFrame({"cat":cat, "s":["a", "c", "c", np.nan]}) df.describe() df["cat"].describe()

Working with categories

Categorical data has a categories and a ordered property, which list their possible values and whether the ordering matters or not. These properties are exposed as s.cat.categories and s.cat.ordered. If you don't manually specify categories and ordering, they are inferred from the passed in values.

python

s = pd.Series(["a","b","c","a"], dtype="category") s.cat.categories s.cat.ordered

It's also possible to pass in the categories in a specific order:

python

s = pd.Series(pd.Categorical(["a","b","c","a"], categories=["c","b","a"])) s.cat.categories s.cat.ordered

Note

New categorical data are NOT automatically ordered. You must explicitly pass ordered=True to indicate an ordered Categorical.

Note

The result of Series.unique() is not always the same as Series.cat.categories, because Series.unique() has a couple of guarantees, namely that it returns categories in the order of appearance, and it only includes values that are actually present.

python

s = pd.Series(list('babc')).astype(CategoricalDtype(list('abcd'))) s

# categories s.cat.categories

# uniques s.unique()

Renaming categories

Renaming categories is done by assigning new values to the Series.cat.categories property or by using the Categorical.rename_categories method:

python

s = pd.Series(["a","b","c","a"], dtype="category") s s.cat.categories = ["Group %s" % g for g in s.cat.categories] s s.cat.rename_categories([1,2,3]) s # You can also pass a dict-like object to map the renaming s.cat.rename_categories({1: 'x', 2: 'y', 3: 'z'}) s

Note

In contrast to R's factor, categorical data can have categories of other types than string.

Note

Be aware that assigning new categories is an inplace operations, while most other operation under Series.cat per default return a new Series of dtype category.

Categories must be unique or a ValueError is raised:

python

try:

s.cat.categories = [1,1,1]

except ValueError as e:

print("ValueError: " + str(e))

Categories must also not be NaN or a ValueError is raised:

python

try:

s.cat.categories = [1,2,np.nan]

except ValueError as e:

print("ValueError: " + str(e))

Appending new categories

Appending categories can be done by using the Categorical.add_categories method:

python

s = s.cat.add_categories([4]) s.cat.categories s

Removing categories

Removing categories can be done by using the Categorical.remove_categories method. Values which are removed are replaced by np.nan.:

python

s = s.cat.remove_categories([4]) s

Removing unused categories

Removing unused categories can also be done:

python

s = pd.Series(pd.Categorical(["a","b","a"], categories=["a","b","c","d"])) s s.cat.remove_unused_categories()

Setting categories

If you want to do remove and add new categories in one step (which has some speed advantage), or simply set the categories to a predefined scale, use Categorical.set_categories.

python

s = pd.Series(["one","two","four", "-"], dtype="category") s s = s.cat.set_categories(["one","two","three","four"]) s

Note

Be aware that Categorical.set_categories cannot know whether some category is omitted intentionally or because it is misspelled or (under Python3) due to a type difference (e.g., numpys S1 dtype and python strings). This can result in surprising behaviour!

Sorting and Order

If categorical data is ordered (s.cat.ordered == True), then the order of the categories has a meaning and certain operations are possible. If the categorical is unordered, .min()/.max() will raise a TypeError.

python

s = pd.Series(pd.Categorical(["a","b","c","a"], ordered=False)) s.sort_values(inplace=True) s = pd.Series(["a","b","c","a"]).astype( CategoricalDtype(ordered=True) ) s.sort_values(inplace=True) s s.min(), s.max()

You can set categorical data to be ordered by using as_ordered() or unordered by using as_unordered(). These will by default return a new object.

python

s.cat.as_ordered() s.cat.as_unordered()

Sorting will use the order defined by categories, not any lexical order present on the data type. This is even true for strings and numeric data:

python

s = pd.Series([1,2,3,1], dtype="category") s = s.cat.set_categories([2,3,1], ordered=True) s s.sort_values(inplace=True) s s.min(), s.max()

Reordering

Reordering the categories is possible via the Categorical.reorder_categories and the Categorical.set_categories methods. For Categorical.reorder_categories, all old categories must be included in the new categories and no new categories are allowed. This will necessarily make the sort order the same as the categories order.

python

s = pd.Series([1,2,3,1], dtype="category") s = s.cat.reorder_categories([2,3,1], ordered=True) s s.sort_values(inplace=True) s s.min(), s.max()

Note

Note the difference between assigning new categories and reordering the categories: the first renames categories and therefore the individual values in the Series, but if the first position was sorted last, the renamed value will still be sorted last. Reordering means that the way values are sorted is different afterwards, but not that individual values in the Series are changed.

Note

If the Categorical is not ordered, Series.min() and Series.max() will raise TypeError. Numeric operations like +, -, *, / and operations based on them (e.g. Series.median(), which would need to compute the mean between two values if the length of an array is even) do not work and raise a TypeError.

Multi Column Sorting

A categorical dtyped column will participate in a multi-column sort in a similar manner to other columns. The ordering of the categorical is determined by the categories of that column.

python

dfs = pd.DataFrame({'A' : pd.Categorical(list('bbeebbaa'), categories=['e','a','b'], ordered=True),

'B' : [1,2,1,2,2,1,2,1] })

dfs.sort_values(by=['A', 'B'])

Reordering the categories changes a future sort.

python

dfs['A'] = dfs['A'].cat.reorder_categories(['a','b','e']) dfs.sort_values(by=['A','B'])

Comparisons

Comparing categorical data with other objects is possible in three cases:

• comparing equality (== and !=) to a list-like object (list, Series, array, ...) of the same length as the categorical data.
• all comparisons (==, !=, >, >=, <, and <=) of categorical data to another categorical Series, when ordered==True and the categories are the same.
• all comparisons of a categorical data to a scalar.

All other comparisons, especially "non-equality" comparisons of two categoricals with different categories or a categorical with any list-like object, will raise a TypeError.

Note

Any "non-equality" comparisons of categorical data with a Series, np.array, list or categorical data with different categories or ordering will raise an TypeError because custom categories ordering could be interpreted in two ways: one with taking into account the ordering and one without.

python

cat = pd.Series([1,2,3]).astype(

CategoricalDtype([3, 2, 1], ordered=True)

) cat_base = pd.Series([2,2,2]).astype( CategoricalDtype([3, 2, 1], ordered=True) ) cat_base2 = pd.Series([2,2,2]).astype( CategoricalDtype(ordered=True) )

cat cat_base cat_base2

Comparing to a categorical with the same categories and ordering or to a scalar works:

python

cat > cat_base cat > 2

Equality comparisons work with any list-like object of same length and scalars:

python

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

This doesn't work because the categories are not the same:

python

try:

cat > cat_base2

except TypeError as e:

print("TypeError: " + str(e))

If you want to do a "non-equality" comparison of a categorical series with a list-like object which is not categorical data, you need to be explicit and convert the categorical data back to the original values:

python

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

try:

cat > base

except TypeError as e:

print("TypeError: " + str(e))

np.asarray(cat) > base

When you compare two unordered categoricals with the same categories, the order is not considered:

python

c1 = pd.Categorical(['a', 'b'], categories=['a', 'b'], ordered=False) c2 = pd.Categorical(['a', 'b'], categories=['b', 'a'], ordered=False) c1 == c2

Operations

Apart from Series.min(), Series.max() and Series.mode(), the following operations are possible with categorical data:

Series methods like Series.value_counts() will use all categories, even if some categories are not present in the data:

python

s = pd.Series(pd.Categorical(["a","b","c","c"], categories=["c","a","b","d"])) s.value_counts()

Groupby will also show "unused" categories:

python

cats = pd.Categorical(["a","b","b","b","c","c","c"], categories=["a","b","c","d"]) df = pd.DataFrame({"cats":cats,"values":[1,2,2,2,3,4,5]}) df.groupby("cats").mean()

cats2 = pd.Categorical(["a","a","b","b"], categories=["a","b","c"]) df2 = pd.DataFrame({"cats":cats2,"B":["c","d","c","d"], "values":[1,2,3,4]}) df2.groupby(["cats","B"]).mean()

Pivot tables:

python

raw_cat = pd.Categorical(["a","a","b","b"], categories=["a","b","c"]) df = pd.DataFrame({"A":raw_cat,"B":["c","d","c","d"], "values":[1,2,3,4]}) pd.pivot_table(df, values='values', index=['A', 'B'])

Data munging

The optimized pandas data access methods .loc, .iloc, .at, and .iat, work as normal. The only difference is the return type (for getting) and that only values already in categories can be assigned.

Getting

If the slicing operation returns either a DataFrame or a column of type Series, the category dtype is preserved.

python

idx = pd.Index(["h","i","j","k","l","m","n",]) cats = pd.Series(["a","b","b","b","c","c","c"], dtype="category", index=idx) values= [1,2,2,2,3,4,5] df = pd.DataFrame({"cats":cats,"values":values}, index=idx) df.iloc[2:4,:] df.iloc[2:4,:].dtypes df.loc["h":"j","cats"] df[df["cats"] == "b"]

An example where the category type is not preserved is if you take one single row: the resulting Series is of dtype object:

python

# get the complete "h" row as a Series df.loc["h", :]

Returning a single item from categorical data will also return the value, not a categorical of length "1".

python

df.iat[0,0] df["cats"].cat.categories = ["x","y","z"] df.at["h","cats"] # returns a string

Note

This is a difference to R's factor function, where factor(c(1,2,3))[1] returns a single value factor.

To get a single value Series of type category pass in a list with a single value:

python

df.loc[["h"],"cats"]

String and datetime accessors

0.17.1

The accessors .dt and .str will work if the s.cat.categories are of an appropriate type:

python

str_s = pd.Series(list('aabb')) str_cat = str_s.astype('category') str_cat str_cat.str.contains("a")

date_s = pd.Series(pd.date_range('1/1/2015', periods=5)) date_cat = date_s.astype('category') date_cat date_cat.dt.day

Note

The returned Series (or DataFrame) is of the same type as if you used the .str.<method> / .dt.<method> on a Series of that type (and not of type category!).

That means, that the returned values from methods and properties on the accessors of a Series and the returned values from methods and properties on the accessors of this Series transformed to one of type category will be equal:

python

ret_s = str_s.str.contains("a") ret_cat = str_cat.str.contains("a") ret_s.dtype == ret_cat.dtype ret_s == ret_cat

Note

The work is done on the categories and then a new Series is constructed. This has some performance implication if you have a Series of type string, where lots of elements are repeated (i.e. the number of unique elements in the Series is a lot smaller than the length of the Series). In this case it can be faster to convert the original Series to one of type category and use .str.<method> or .dt.<property> on that.

Setting

Setting values in a categorical column (or Series) works as long as the value is included in the `categories`:

python

idx = pd.Index(["h","i","j","k","l","m","n"]) cats = pd.Categorical(["a","a","a","a","a","a","a"], categories=["a","b"]) values = [1,1,1,1,1,1,1] df = pd.DataFrame({"cats":cats,"values":values}, index=idx)

df.iloc[2:4,:] = [["b",2],["b",2]] df try: df.iloc[2:4,:] = [["c",3],["c",3]] except ValueError as e: print("ValueError: " + str(e))

Setting values by assigning categorical data will also check that the categories match:

python

df.loc["j":"k","cats"] = pd.Categorical(["a","a"], categories=["a","b"]) df try: df.loc["j":"k","cats"] = pd.Categorical(["b","b"], categories=["a","b","c"]) except ValueError as e: print("ValueError: " + str(e))

Assigning a Categorical to parts of a column of other types will use the values:

python

df = pd.DataFrame({"a":[1,1,1,1,1], "b":["a","a","a","a","a"]}) df.loc[1:2,"a"] = pd.Categorical(["b","b"], categories=["a","b"]) df.loc[2:3,"b"] = pd.Categorical(["b","b"], categories=["a","b"]) df df.dtypes

Merging

You can concat two DataFrames containing categorical data together, but the categories of these categoricals need to be the same:

python

cat = pd.Series(["a","b"], dtype="category") vals = [1,2] df = pd.DataFrame({"cats":cat, "vals":vals}) res = pd.concat([df,df]) res res.dtypes

In this case the categories are not the same and so an error is raised:

python

df_different = df.copy() df_different["cats"].cat.categories = ["c","d"] try: pd.concat([df,df_different]) except ValueError as e: print("ValueError: " + str(e))

The same applies to df.append(df_different).

See also the section on merge dtypes<merging.dtypes> for notes about preserving merge dtypes and performance.

Unioning

0.19.0

If you want to combine categoricals that do not necessarily have the same categories, the union_categoricals function will combine a list-like of categoricals. The new categories will be the union of the categories being combined.

python

from pandas.api.types import union_categoricals a = pd.Categorical(["b", "c"]) b = pd.Categorical(["a", "b"]) union_categoricals([a, b])

By default, the resulting categories will be ordered as they appear in the data. If you want the categories to be lexsorted, use sort_categories=True argument.

python

union_categoricals([a, b], sort_categories=True)

union_categoricals also works with the "easy" case of combining two categoricals of the same categories and order information (e.g. what you could also append for).

python

a = pd.Categorical(["a", "b"], ordered=True) b = pd.Categorical(["a", "b", "a"], ordered=True) union_categoricals([a, b])

The below raises TypeError because the categories are ordered and not identical.

In [1]: a = pd.Categorical(["a", "b"], ordered=True)
In [2]: b = pd.Categorical(["a", "b", "c"], ordered=True)
In [3]: union_categoricals([a, b])
Out[3]:
TypeError: to union ordered Categoricals, all categories must be the same

0.20.0

Ordered categoricals with different categories or orderings can be combined by using the ignore_ordered=True argument.

python

a = pd.Categorical(["a", "b", "c"], ordered=True) b = pd.Categorical(["c", "b", "a"], ordered=True) union_categoricals([a, b], ignore_order=True)

union_categoricals also works with a CategoricalIndex, or Series containing categorical data, but note that the resulting array will always be a plain Categorical

python

a = pd.Series(["b", "c"], dtype='category') b = pd.Series(["a", "b"], dtype='category') union_categoricals([a, b])

Note

union_categoricals may recode the integer codes for categories when combining categoricals. This is likely what you want, but if you are relying on the exact numbering of the categories, be aware.

python

c1 = pd.Categorical(["b", "c"]) c2 = pd.Categorical(["a", "b"])

c1 # "b" is coded to 0 c1.codes

c2 # "b" is coded to 1 c2.codes

c = union_categoricals([c1, c2]) c # "b" is coded to 0 throughout, same as c1, different from c2 c.codes

Concatenation

This section describes concatenations specific to category dtype. See Concatenating objects<merging.concat> for general description.

By default, Series or DataFrame concatenation which contains the same categories results in category dtype, otherwise results in object dtype. Use .astype or union_categoricals to get category result.

python

# same categories s1 = pd.Series(['a', 'b'], dtype='category') s2 = pd.Series(['a', 'b', 'a'], dtype='category') pd.concat([s1, s2])

# different categories s3 = pd.Series(['b', 'c'], dtype='category') pd.concat([s1, s3])

pd.concat([s1, s3]).astype('category') union_categoricals([s1.values, s3.values])

Following table summarizes the results of Categoricals related concatenations.

arg1	arg2	result
category	category (identical categories)	category
category	category (different categories, both not ordered)	object (dtype is inferred)
category	category (different categories, either one is ordered)	object (dtype is inferred)
category	not category	object (dtype is inferred)

Getting Data In/Out

You can write data that contains category dtypes to a HDFStore. See here <io.hdf5-categorical> for an example and caveats.

It is also possible to write data to and reading data from Stata format files. See here <io.stata-categorical> for an example and caveats.

Writing to a CSV file will convert the data, effectively removing any information about the categorical (categories and ordering). So if you read back the CSV file you have to convert the relevant columns back to category and assign the right categories and categories ordering.

python

from pandas.compat import StringIO

python

s = pd.Series(pd.Categorical(['a', 'b', 'b', 'a', 'a', 'd'])) # rename the categories s.cat.categories = ["very good", "good", "bad"] # reorder the categories and add missing categories s = s.cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) df = pd.DataFrame({"cats":s, "vals":[1,2,3,4,5,6]}) csv = StringIO() df.to_csv(csv) df2 = pd.read_csv(StringIO(csv.getvalue())) df2.dtypes df2["cats"] # Redo the category df2["cats"] = df2["cats"].astype("category") df2["cats"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"], inplace=True) df2.dtypes df2["cats"]

The same holds for writing to a SQL database with to_sql.

Missing Data

pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section <missing_data>.

Missing values should not be included in the Categorical's categories, only in the values. Instead, it is understood that NaN is different, and is always a possibility. When working with the Categorical's codes, missing values will always have a code of -1.

python

s = pd.Series(["a", "b", np.nan, "a"], dtype="category") # only two categories s s.cat.codes

Methods for working with missing data, e.g. ~Series.isna, ~Series.fillna, ~Series.dropna, all work normally:

python

s = pd.Series(["a", "b", np.nan], dtype="category") s pd.isna(s) s.fillna("a")

Differences to R's factor

The following differences to R's factor functions can be observed:

• R's levels are named categories
• R's levels are always of type string, while categories in pandas can be of any dtype.
• It's not possible to specify labels at creation time. Use s.cat.rename_categories(new_labels) afterwards.
• In contrast to R's factor function, using categorical data as the sole input to create a new categorical series will not remove unused categories but create a new categorical series which is equal to the passed in one!
• R allows for missing values to be included in its levels (pandas' categories). Pandas does not allow NaN categories, but missing values can still be in the values.

Gotchas

Memory Usage

The memory usage of a Categorical is proportional to the number of categories plus the length of the data. In contrast, an object dtype is a constant times the length of the data.

python

s = pd.Series(['foo','bar']*1000)

# object dtype s.nbytes

# category dtype s.astype('category').nbytes

Note

If the number of categories approaches the length of the data, the Categorical will use nearly the same or more memory than an equivalent object dtype representation.

python

s = pd.Series(['foo%04d' % i for i in range(2000)])

# object dtype s.nbytes

# category dtype s.astype('category').nbytes

Categorical is not a numpy array

Currently, categorical data and the underlying Categorical is implemented as a python object and not as a low-level numpy array dtype. This leads to some problems.

numpy itself doesn't know about the new `dtype`:

python

try:

np.dtype("category")

except TypeError as e:

print("TypeError: " + str(e))

dtype = pd.Categorical(["a"]).dtype try: np.dtype(dtype) except TypeError as e: print("TypeError: " + str(e))

Dtype comparisons work:

python

dtype == np.str np.str == dtype

To check if a Series contains Categorical data, use hasattr(s, 'cat'):

python

hasattr(pd.Series(['a'], dtype='category'), 'cat') hasattr(pd.Series(['a']), 'cat')

Using numpy functions on a Series of type category should not work as Categoricals are not numeric data (even in the case that .categories is numeric).

python

s = pd.Series(pd.Categorical([1,2,3,4])) try: np.sum(s) #same with np.log(s),.. except TypeError as e: print("TypeError: " + str(e))

Note

If such a function works, please file a bug at https://github.com/pandas-dev/pandas!

dtype in apply

Pandas currently does not preserve the dtype in apply functions: If you apply along rows you get a Series of object dtype (same as getting a row -> getting one element will return a basic type) and applying along columns will also convert to object.

python

df = pd.DataFrame({"a":[1,2,3,4],

"b":["a","b","c","d"], "cats":pd.Categorical([1,2,3,2])})

df.apply(lambda row: type(row["cats"]), axis=1) df.apply(lambda col: col.dtype, axis=0)

Categorical Index

CategoricalIndex is a type of index that is useful for supporting indexing with duplicates. This is a container around a Categorical and allows efficient indexing and storage of an index with a large number of duplicated elements. See the advanced indexing docs <indexing.categoricalindex> for a more detailed explanation.

Setting the index will create a CategoricalIndex

python

cats = pd.Categorical([1,2,3,4], categories=[4,2,3,1]) strings = ["a","b","c","d"] values = [4,2,3,1] df = pd.DataFrame({"strings":strings, "values":values}, index=cats) df.index # This now sorts by the categories order df.sort_index()

Side Effects

Constructing a Series from a Categorical will not copy the input Categorical. This means that changes to the Series will in most cases change the original `Categorical`:

python

cat = pd.Categorical([1,2,3,10], categories=[1,2,3,4,10]) s = pd.Series(cat, name="cat") cat s.iloc[0:2] = 10 cat df = pd.DataFrame(s) df["cat"].cat.categories = [1,2,3,4,5] cat

Use copy=True to prevent such a behaviour or simply don't reuse `Categoricals`:

python

cat = pd.Categorical([1,2,3,10], categories=[1,2,3,4,10]) s = pd.Series(cat, name="cat", copy=True) cat s.iloc[0:2] = 10 cat

Note

This also happens in some cases when you supply a numpy array instead of a `Categorical`: using an int array (e.g. np.array([1,2,3,4])) will exhibit the same behaviour, while using a string array (e.g. np.array(["a","b","c","a"])) will not.