Categorical data — pandas 2.2.3 documentation (2024)

This is an introduction to pandas categorical data type, including a short comparisonwith R’s factor.

Categoricals are a pandas data type corresponding to categorical variables instatistics. A categorical variable takes on a limited, and usually fixed,number of possible values (categories; levels in R). Examples are gender,social class, blood type, country affiliation, observation time or rating viaLikert 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 numericaloperations (additions, divisions, …) are not possible.

All values of categorical data are either in categories or np.nan. Order is defined bythe order of categories, not lexical order of the values. Internally, the data structureconsists of a categories array and an integer array of codes which point to the real value inthe 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 stringvariable to a categorical variable will save some memory, see here.
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 andmin/max will use the logical order instead of the lexical order, see here.
As a signal to other Python libraries that this column should be treated as a categoricalvariable (e.g. to use suitable statistical methods or plot types).

Object creation#

Series creation#

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

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

In [1]: s = pd.Series(["a", "b", "c", "a"], dtype="category")In [2]: sOut[2]: 0 a1 b2 c3 adtype: categoryCategories (3, object): ['a', 'b', 'c']

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

In [3]: df = pd.DataFrame({"A": ["a", "b", "c", "a"]})In [4]: df["B"] = df["A"].astype("category")In [5]: dfOut[5]:  A B0 a a1 b b2 c c3 a a

By using special functions, such as cut(), which groups data intodiscrete bins. See the example on tiling in the docs.

In [6]: df = pd.DataFrame({"value": np.random.randint(0, 100, 20)})In [7]: labels = ["{0} - {1}".format(i, i + 9) for i in range(0, 100, 10)]In [8]: df["group"] = pd.cut(df.value, range(0, 105, 10), right=False, labels=labels)In [9]: df.head(10)Out[9]:  value group0 65 60 - 691 49 40 - 492 56 50 - 593 43 40 - 494 43 40 - 495 91 90 - 996 32 30 - 397 87 80 - 898 36 30 - 399 8 0 - 9

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

In [10]: raw_cat = pd.Categorical( ....:  ["a", "b", "c", "a"], categories=["b", "c", "d"], ordered=False ....: ) ....: In [11]: s = pd.Series(raw_cat)In [12]: sOut[12]: 0 NaN1 b2 c3 NaNdtype: categoryCategories (3, object): ['b', 'c', 'd']In [13]: df = pd.DataFrame({"A": ["a", "b", "c", "a"]})In [14]: df["B"] = raw_catIn [15]: dfOut[15]:  A B0 a NaN1 b b2 c c3 a NaN

Categorical data has a specific category dtype:

In [16]: df.dtypesOut[16]: A objectB categorydtype: object

DataFrame creation#

Similar to the previous section where a single column was converted to categorical, all columns in aDataFrame can be batch converted to categorical either during or after construction.

This can be done during construction by specifying dtype="category" in the DataFrame constructor:

In [17]: df = pd.DataFrame({"A": list("abca"), "B": list("bccd")}, dtype="category")In [18]: df.dtypesOut[18]: A categoryB categorydtype: object

Note that the categories present in each column differ; the conversion is done column by column, soonly labels present in a given column are categories:

In [19]: df["A"]Out[19]: 0 a1 b2 c3 aName: A, dtype: categoryCategories (3, object): ['a', 'b', 'c']In [20]: df["B"]Out[20]: 0 b1 c2 c3 dName: B, dtype: categoryCategories (3, object): ['b', 'c', 'd']

Analogously, all columns in an existing DataFrame can be batch converted using DataFrame.astype():

In [21]: df = pd.DataFrame({"A": list("abca"), "B": list("bccd")})In [22]: df_cat = df.astype("category")In [23]: df_cat.dtypesOut[23]: A categoryB categorydtype: object

This conversion is likewise done column by column:

In [24]: df_cat["A"]Out[24]: 0 a1 b2 c3 aName: A, dtype: categoryCategories (3, object): ['a', 'b', 'c']In [25]: df_cat["B"]Out[25]: 0 b1 c2 c3 dName: B, dtype: categoryCategories (3, object): ['b', 'c', 'd']

Controlling behavior#

In the examples above where we passed dtype='category', we used the defaultbehavior:

Categories are inferred from the data.
Categories are unordered.

To control those behaviors, instead of passing 'category', use an instanceof CategoricalDtype.

In [26]: from pandas.api.types import CategoricalDtypeIn [27]: s = pd.Series(["a", "b", "c", "a"])In [28]: cat_type = CategoricalDtype(categories=["b", "c", "d"], ordered=True)In [29]: s_cat = s.astype(cat_type)In [30]: s_catOut[30]: 0 NaN1 b2 c3 NaNdtype: categoryCategories (3, object): ['b' < 'c' < 'd']

Similarly, a CategoricalDtype can be used with a DataFrame to ensure that categoriesare consistent among all columns.

In [31]: from pandas.api.types import CategoricalDtypeIn [32]: df = pd.DataFrame({"A": list("abca"), "B": list("bccd")})In [33]: cat_type = CategoricalDtype(categories=list("abcd"), ordered=True)In [34]: df_cat = df.astype(cat_type)In [35]: df_cat["A"]Out[35]: 0 a1 b2 c3 aName: A, dtype: categoryCategories (4, object): ['a' < 'b' < 'c' < 'd']In [36]: df_cat["B"]Out[36]: 0 b1 c2 c3 dName: B, dtype: categoryCategories (4, object): ['a' < 'b' < 'c' < 'd']

Note

To perform table-wise conversion, where all labels in the entire DataFrame are used ascategories for each column, the categories parameter can be determined programmatically bycategories = pd.unique(df.to_numpy().ravel()).

If you already have codes and categories, you can use thefrom_codes() constructor to save the factorize stepduring normal constructor mode:

In [37]: splitter = np.random.choice([0, 1], 5, p=[0.5, 0.5])In [38]: s = pd.Series(pd.Categorical.from_codes(splitter, categories=["train", "test"]))

Regaining original data#

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

In [39]: s = pd.Series(["a", "b", "c", "a"])In [40]: sOut[40]: 0 a1 b2 c3 adtype: objectIn [41]: s2 = s.astype("category")In [42]: s2Out[42]: 0 a1 b2 c3 adtype: categoryCategories (3, object): ['a', 'b', 'c']In [43]: s2.astype(str)Out[43]: 0 a1 b2 c3 adtype: objectIn [44]: np.asarray(s2)Out[44]: array(['a', 'b', 'c', 'a'], dtype=object)

Note

In contrast to R’s factor function, categorical data is not converting input values tostrings; 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 atcreation time. Use categories to change the categories after creation time.

CategoricalDtype#

A categorical’s type is fully described by

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

This information can be stored in a CategoricalDtype.The categories argument is optional, which implies that the actual categoriesshould be inferred from whatever is present in the data when thepandas.Categorical is created. The categories are assumed to be unorderedby default.

In [45]: from pandas.api.types import CategoricalDtypeIn [46]: CategoricalDtype(["a", "b", "c"])Out[46]: CategoricalDtype(categories=['a', 'b', 'c'], ordered=False, categories_dtype=object)In [47]: CategoricalDtype(["a", "b", "c"], ordered=True)Out[47]: CategoricalDtype(categories=['a', 'b', 'c'], ordered=True, categories_dtype=object)In [48]: CategoricalDtype()Out[48]: CategoricalDtype(categories=None, ordered=False, categories_dtype=None)

A CategoricalDtype can be used in any place pandasexpects 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 aCategoricalDtype when you want the default behavior ofthe categories being unordered, and equal to the set values present in thearray. In other words, dtype='category' is equivalent todtype=CategoricalDtype().

Equality semantics#

Two instances of CategoricalDtype compare equalwhenever they have the same categories and order. When comparing twounordered categoricals, the order of the categories is not considered.

In [49]: c1 = CategoricalDtype(["a", "b", "c"], ordered=False)# Equal, since order is not considered when ordered=FalseIn [50]: c1 == CategoricalDtype(["b", "c", "a"], ordered=False)Out[50]: True# Unequal, since the second CategoricalDtype is orderedIn [51]: c1 == CategoricalDtype(["a", "b", "c"], ordered=True)Out[51]: False

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

In [52]: c1 == "category"Out[52]: True

Description#

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

In [53]: cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])In [54]: df = pd.DataFrame({"cat": cat, "s": ["a", "c", "c", np.nan]})In [55]: df.describe()Out[55]:  cat scount 3 3unique 2 2top c cfreq 2 2In [56]: df["cat"].describe()Out[56]: count 3unique 2top cfreq 2Name: cat, dtype: object

Working with categories#

Categorical data has a categories and a ordered property, which list theirpossible values and whether the ordering matters or not. These properties areexposed as s.cat.categories and s.cat.ordered. If you don’t manuallyspecify categories and ordering, they are inferred from the passed arguments.

In [57]: s = pd.Series(["a", "b", "c", "a"], dtype="category")In [58]: s.cat.categoriesOut[58]: Index(['a', 'b', 'c'], dtype='object')In [59]: s.cat.orderedOut[59]: False

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

In [60]: s = pd.Series(pd.Categorical(["a", "b", "c", "a"], categories=["c", "b", "a"]))In [61]: s.cat.categoriesOut[61]: Index(['c', 'b', 'a'], dtype='object')In [62]: s.cat.orderedOut[62]: False

Note

New categorical data are not automatically ordered. You must explicitlypass ordered=True to indicate an ordered Categorical.

Note

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

In [63]: s = pd.Series(list("babc")).astype(CategoricalDtype(list("abcd")))In [64]: sOut[64]: 0 b1 a2 b3 cdtype: categoryCategories (4, object): ['a', 'b', 'c', 'd']# categoriesIn [65]: s.cat.categoriesOut[65]: Index(['a', 'b', 'c', 'd'], dtype='object')# uniquesIn [66]: s.unique()Out[66]: ['b', 'a', 'c']Categories (4, object): ['a', 'b', 'c', 'd']

Renaming categories#

Renaming categories is done by using therename_categories() method:

In [67]: s = pd.Series(["a", "b", "c", "a"], dtype="category")In [68]: sOut[68]: 0 a1 b2 c3 adtype: categoryCategories (3, object): ['a', 'b', 'c']In [69]: new_categories = ["Group %s" % g for g in s.cat.categories]In [70]: s = s.cat.rename_categories(new_categories)In [71]: sOut[71]: 0 Group a1 Group b2 Group c3 Group adtype: categoryCategories (3, object): ['Group a', 'Group b', 'Group c']# You can also pass a dict-like object to map the renamingIn [72]: s = s.cat.rename_categories({1: "x", 2: "y", 3: "z"})In [73]: sOut[73]: 0 Group a1 Group b2 Group c3 Group adtype: categoryCategories (3, object): ['Group a', 'Group b', 'Group c']

Note

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

Categories must be unique or a ValueError is raised:

In [74]: try: ....:  s = s.cat.rename_categories([1, 1, 1]) ....: except ValueError as e: ....:  print("ValueError:", str(e)) ....: ValueError: Categorical categories must be unique

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

In [75]: try: ....:  s = s.cat.rename_categories([1, 2, np.nan]) ....: except ValueError as e: ....:  print("ValueError:", str(e)) ....: ValueError: Categorical categories cannot be null

Appending new categories#

Appending categories can be done by using theadd_categories() method:

In [76]: s = s.cat.add_categories([4])In [77]: s.cat.categoriesOut[77]: Index(['Group a', 'Group b', 'Group c', 4], dtype='object')In [78]: sOut[78]: 0 Group a1 Group b2 Group c3 Group adtype: categoryCategories (4, object): ['Group a', 'Group b', 'Group c', 4]

Removing categories#

Removing categories can be done by using theremove_categories() method. Values which are removedare replaced by np.nan.:

In [79]: s = s.cat.remove_categories([4])In [80]: sOut[80]: 0 Group a1 Group b2 Group c3 Group adtype: categoryCategories (3, object): ['Group a', 'Group b', 'Group c']

Removing unused categories#

Removing unused categories can also be done:

In [81]: s = pd.Series(pd.Categorical(["a", "b", "a"], categories=["a", "b", "c", "d"]))In [82]: sOut[82]: 0 a1 b2 adtype: categoryCategories (4, object): ['a', 'b', 'c', 'd']In [83]: s.cat.remove_unused_categories()Out[83]: 0 a1 b2 adtype: categoryCategories (2, object): ['a', 'b']

Setting categories#

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

In [84]: s = pd.Series(["one", "two", "four", "-"], dtype="category")In [85]: sOut[85]: 0 one1 two2 four3 -dtype: categoryCategories (4, object): ['-', 'four', 'one', 'two']In [86]: s = s.cat.set_categories(["one", "two", "three", "four"])In [87]: sOut[87]: 0 one1 two2 four3 NaNdtype: categoryCategories (4, object): ['one', 'two', 'three', 'four']

Note

Be aware that Categorical.set_categories() cannot know whether some category is omittedintentionally or because it is misspelled or (under Python3) due to a type difference (e.g.,NumPy 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 ameaning and certain operations are possible. If the categorical is unordered, .min()/.max() will raise a TypeError.

In [88]: s = pd.Series(pd.Categorical(["a", "b", "c", "a"], ordered=False))In [89]: s = s.sort_values()In [90]: s = pd.Series(["a", "b", "c", "a"]).astype(CategoricalDtype(ordered=True))In [91]: s = s.sort_values()In [92]: sOut[92]: 0 a3 a1 b2 cdtype: categoryCategories (3, object): ['a' < 'b' < 'c']In [93]: s.min(), s.max()Out[93]: ('a', 'c')

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

In [94]: s.cat.as_ordered()Out[94]: 0 a3 a1 b2 cdtype: categoryCategories (3, object): ['a' < 'b' < 'c']In [95]: s.cat.as_unordered()Out[95]: 0 a3 a1 b2 cdtype: categoryCategories (3, object): ['a', 'b', 'c']

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:

In [96]: s = pd.Series([1, 2, 3, 1], dtype="category")In [97]: s = s.cat.set_categories([2, 3, 1], ordered=True)In [98]: sOut[98]: 0 11 22 33 1dtype: categoryCategories (3, int64): [2 < 3 < 1]In [99]: s = s.sort_values()In [100]: sOut[100]: 1 22 30 13 1dtype: categoryCategories (3, int64): [2 < 3 < 1]In [101]: s.min(), s.max()Out[101]: (2, 1)

Reordering#

Reordering the categories is possible via the Categorical.reorder_categories() andthe Categorical.set_categories() methods. For Categorical.reorder_categories(), allold categories must be included in the new categories and no new categories are allowed. This willnecessarily make the sort order the same as the categories order.

In [102]: s = pd.Series([1, 2, 3, 1], dtype="category")In [103]: s = s.cat.reorder_categories([2, 3, 1], ordered=True)In [104]: sOut[104]: 0 11 22 33 1dtype: categoryCategories (3, int64): [2 < 3 < 1]In [105]: s = s.sort_values()In [106]: sOut[106]: 1 22 30 13 1dtype: categoryCategories (3, int64): [2 < 3 < 1]In [107]: s.min(), s.max()Out[107]: (2, 1)

Note

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

Note

If the Categorical is not ordered, Series.min() and Series.max() will raiseTypeError. Numeric operations like +, -, *, / and operations based on them(e.g. Series.median(), which would need to compute the mean between two values if the lengthof 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.

In [108]: dfs = pd.DataFrame( .....:  { .....:  "A": pd.Categorical( .....:  list("bbeebbaa"), .....:  categories=["e", "a", "b"], .....:  ordered=True, .....:  ), .....:  "B": [1, 2, 1, 2, 2, 1, 2, 1], .....:  } .....: ) .....: In [109]: dfs.sort_values(by=["A", "B"])Out[109]:  A B2 e 13 e 27 a 16 a 20 b 15 b 11 b 24 b 2

Reordering the categories changes a future sort.

In [110]: dfs["A"] = dfs["A"].cat.reorder_categories(["a", "b", "e"])In [111]: dfs.sort_values(by=["A", "B"])Out[111]:  A B7 a 16 a 20 b 15 b 11 b 24 b 22 e 13 e 2

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 toanother 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 differentcategories or a categorical with any list-like object, will raise a TypeError.

Note

Operations#

Apart from Series.min(), Series.max() and Series.mode(), thefollowing 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:

In [130]: s = pd.Series(pd.Categorical(["a", "b", "c", "c"], categories=["c", "a", "b", "d"]))In [131]: s.value_counts()Out[131]: c 2a 1b 1d 0Name: count, dtype: int64

DataFrame methods like DataFrame.sum() also show “unused” categories when observed=False.

In [132]: columns = pd.Categorical( .....:  ["One", "One", "Two"], categories=["One", "Two", "Three"], ordered=True .....: ) .....: In [133]: df = pd.DataFrame( .....:  data=[[1, 2, 3], [4, 5, 6]], .....:  columns=pd.MultiIndex.from_arrays([["A", "B", "B"], columns]), .....: ).T .....: In [134]: df.groupby(level=1, observed=False).sum()Out[134]:  0 1One 3 9Two 3 6Three 0 0

Groupby will also show “unused” categories when observed=False:

In [135]: cats = pd.Categorical( .....:  ["a", "b", "b", "b", "c", "c", "c"], categories=["a", "b", "c", "d"] .....: ) .....: In [136]: df = pd.DataFrame({"cats": cats, "values": [1, 2, 2, 2, 3, 4, 5]})In [137]: df.groupby("cats", observed=False).mean()Out[137]:  valuescats a 1.0b 2.0c 4.0d NaNIn [138]: cats2 = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])In [139]: df2 = pd.DataFrame( .....:  { .....:  "cats": cats2, .....:  "B": ["c", "d", "c", "d"], .....:  "values": [1, 2, 3, 4], .....:  } .....: ) .....: In [140]: df2.groupby(["cats", "B"], observed=False).mean()Out[140]:  valuescats B a c 1.0 d 2.0b c 3.0 d 4.0c c NaN d NaN

Pivot tables:

In [141]: raw_cat = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])In [142]: df = pd.DataFrame({"A": raw_cat, "B": ["c", "d", "c", "d"], "values": [1, 2, 3, 4]})In [143]: pd.pivot_table(df, values="values", index=["A", "B"], observed=False)Out[143]:  valuesA B a c 1.0 d 2.0b c 3.0 d 4.0

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) andthat only values already in categories can be assigned.

Getting#

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

In [144]: idx = pd.Index(["h", "i", "j", "k", "l", "m", "n"])In [145]: cats = pd.Series(["a", "b", "b", "b", "c", "c", "c"], dtype="category", index=idx)In [146]: values = [1, 2, 2, 2, 3, 4, 5]In [147]: df = pd.DataFrame({"cats": cats, "values": values}, index=idx)In [148]: df.iloc[2:4, :]Out[148]:  cats valuesj b 2k b 2In [149]: df.iloc[2:4, :].dtypesOut[149]: cats categoryvalues int64dtype: objectIn [150]: df.loc["h":"j", "cats"]Out[150]: h ai bj bName: cats, dtype: categoryCategories (3, object): ['a', 'b', 'c']In [151]: df[df["cats"] == "b"]Out[151]:  cats valuesi b 2j b 2k b 2

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

# get the complete "h" row as a SeriesIn [152]: df.loc["h", :]Out[152]: cats avalues 1Name: h, dtype: object

Returning a single item from categorical data will also return the value, not a categoricalof length “1”.

In [153]: df.iat[0, 0]Out[153]: 'a'In [154]: df["cats"] = df["cats"].cat.rename_categories(["x", "y", "z"])In [155]: df.at["h", "cats"] # returns a stringOut[155]: 'x'

Note

The is in contrast 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, you pass in a list witha single value:

In [156]: df.loc[["h"], "cats"]Out[156]: h xName: cats, dtype: categoryCategories (3, object): ['x', 'y', 'z']

String and datetime accessors#

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

In [157]: str_s = pd.Series(list("aabb"))In [158]: str_cat = str_s.astype("category")In [159]: str_catOut[159]: 0 a1 a2 b3 bdtype: categoryCategories (2, object): ['a', 'b']In [160]: str_cat.str.contains("a")Out[160]: 0 True1 True2 False3 Falsedtype: boolIn [161]: date_s = pd.Series(pd.date_range("1/1/2015", periods=5))In [162]: date_cat = date_s.astype("category")In [163]: date_catOut[163]: 0 2015-01-011 2015-01-022 2015-01-033 2015-01-044 2015-01-05dtype: categoryCategories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03, 2015-01-04, 2015-01-05]In [164]: date_cat.dt.dayOut[164]: 0 11 22 33 44 5dtype: int32

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 oftype category!).

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

In [165]: ret_s = str_s.str.contains("a")In [166]: ret_cat = str_cat.str.contains("a")In [167]: ret_s.dtype == ret_cat.dtypeOut[167]: TrueIn [168]: ret_s == ret_catOut[168]: 0 True1 True2 True3 Truedtype: bool

Note

The work is done on the categories and then a new Series is constructed. This hassome performance implication if you have a Series of type string, where lots of elementsare repeated (i.e. the number of unique elements in the Series is a lot smaller than thelength of the Series). In this case it can be faster to convert the original Seriesto 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 thevalue is included in the categories:

In [169]: idx = pd.Index(["h", "i", "j", "k", "l", "m", "n"])In [170]: cats = pd.Categorical(["a", "a", "a", "a", "a", "a", "a"], categories=["a", "b"])In [171]: values = [1, 1, 1, 1, 1, 1, 1]In [172]: df = pd.DataFrame({"cats": cats, "values": values}, index=idx)In [173]: df.iloc[2:4, :] = [["b", 2], ["b", 2]]In [174]: dfOut[174]:  cats valuesh a 1i a 1j b 2k b 2l a 1m a 1n a 1In [175]: try: .....:  df.iloc[2:4, :] = [["c", 3], ["c", 3]] .....: except TypeError as e: .....:  print("TypeError:", str(e)) .....: TypeError: Cannot setitem on a Categorical with a new category, set the categories first

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

In [176]: df.loc["j":"k", "cats"] = pd.Categorical(["a", "a"], categories=["a", "b"])In [177]: dfOut[177]:  cats valuesh a 1i a 1j a 2k a 2l a 1m a 1n a 1In [178]: try: .....:  df.loc["j":"k", "cats"] = pd.Categorical(["b", "b"], categories=["a", "b", "c"]) .....: except TypeError as e: .....:  print("TypeError:", str(e)) .....: TypeError: Cannot set a Categorical with another, without identical categories

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

In [179]: df = pd.DataFrame({"a": [1, 1, 1, 1, 1], "b": ["a", "a", "a", "a", "a"]})In [180]: df.loc[1:2, "a"] = pd.Categorical(["b", "b"], categories=["a", "b"])In [181]: df.loc[2:3, "b"] = pd.Categorical(["b", "b"], categories=["a", "b"])In [182]: dfOut[182]:  a b0 1 a1 b a2 b b3 1 b4 1 aIn [183]: df.dtypesOut[183]: a objectb objectdtype: object

Merging / concatenation#

By default, combining Series or DataFrames which contain the samecategories results in category dtype, otherwise results will depend on thedtype of the underlying categories. Merges that result in non-categoricaldtypes will likely have higher memory usage. Use .astype orunion_categoricals to ensure category results.

In [184]: from pandas.api.types import union_categoricals# same categoriesIn [185]: s1 = pd.Series(["a", "b"], dtype="category")In [186]: s2 = pd.Series(["a", "b", "a"], dtype="category")In [187]: pd.concat([s1, s2])Out[187]: 0 a1 b0 a1 b2 adtype: categoryCategories (2, object): ['a', 'b']# different categoriesIn [188]: s3 = pd.Series(["b", "c"], dtype="category")In [189]: pd.concat([s1, s3])Out[189]: 0 a1 b0 b1 cdtype: object# Output dtype is inferred based on categories valuesIn [190]: int_cats = pd.Series([1, 2], dtype="category")In [191]: float_cats = pd.Series([3.0, 4.0], dtype="category")In [192]: pd.concat([int_cats, float_cats])Out[192]: 0 1.01 2.00 3.01 4.0dtype: float64In [193]: pd.concat([s1, s3]).astype("category")Out[193]: 0 a1 b0 b1 cdtype: categoryCategories (3, object): ['a', 'b', 'c']In [194]: union_categoricals([s1.array, s3.array])Out[194]: ['a', 'b', 'b', 'c']Categories (3, object): ['a', 'b', 'c']

The following table summarizes the results of merging Categoricals:

arg1	arg2	identical	result
category	category	True	category
category (object)	category (object)	False	object (dtype is inferred)
category (int)	category (float)	False	float (dtype is inferred)

Unioning#

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

In [195]: from pandas.api.types import union_categoricalsIn [196]: a = pd.Categorical(["b", "c"])In [197]: b = pd.Categorical(["a", "b"])In [198]: union_categoricals([a, b])Out[198]: ['b', 'c', 'a', 'b']Categories (3, object): ['b', 'c', 'a']

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

In [199]: union_categoricals([a, b], sort_categories=True)Out[199]: ['b', 'c', 'a', 'b']Categories (3, object): ['a', 'b', 'c']

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

In [200]: a = pd.Categorical(["a", "b"], ordered=True)In [201]: b = pd.Categorical(["a", "b", "a"], ordered=True)In [202]: union_categoricals([a, b])Out[202]: ['a', 'b', 'a', 'b', 'a']Categories (2, object): ['a' < 'b']

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

In [203]: a = pd.Categorical(["a", "b"], ordered=True)In [204]: b = pd.Categorical(["a", "b", "c"], ordered=True)In [205]: union_categoricals([a, b])---------------------------------------------------------------------------TypeError Traceback (most recent call last)Cell In[205], line 1----> 1 union_categoricals([a, b])File ~/work/pandas/pandas/pandas/core/dtypes/concat.py:341, in union_categoricals(to_union, sort_categories, ignore_order) 339 if all(c.ordered for c in to_union): 340 msg = "to union ordered Categoricals, all categories must be the same"--> 341 raise TypeError(msg) 342 raise TypeError("Categorical.ordered must be the same") 344 if ignore_order:TypeError: to union ordered Categoricals, all categories must be the same

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

In [206]: a = pd.Categorical(["a", "b", "c"], ordered=True)In [207]: b = pd.Categorical(["c", "b", "a"], ordered=True)In [208]: union_categoricals([a, b], ignore_order=True)Out[208]: ['a', 'b', 'c', 'c', 'b', 'a']Categories (3, object): ['a', 'b', 'c']

union_categoricals() also works with aCategoricalIndex, or Series containing categorical data, but note thatthe resulting array will always be a plain Categorical:

In [209]: a = pd.Series(["b", "c"], dtype="category")In [210]: b = pd.Series(["a", "b"], dtype="category")In [211]: union_categoricals([a, b])Out[211]: ['b', 'c', 'a', 'b']Categories (3, object): ['b', 'c', 'a']

Note

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

In [212]: c1 = pd.Categorical(["b", "c"])In [213]: c2 = pd.Categorical(["a", "b"])In [214]: c1Out[214]: ['b', 'c']Categories (2, object): ['b', 'c']# "b" is coded to 0In [215]: c1.codesOut[215]: array([0, 1], dtype=int8)In [216]: c2Out[216]: ['a', 'b']Categories (2, object): ['a', 'b']# "b" is coded to 1In [217]: c2.codesOut[217]: array([0, 1], dtype=int8)In [218]: c = union_categoricals([c1, c2])In [219]: cOut[219]: ['b', 'c', 'a', 'b']Categories (3, object): ['b', 'c', 'a']# "b" is coded to 0 throughout, same as c1, different from c2In [220]: c.codesOut[220]: array([0, 1, 2, 0], dtype=int8)

Getting data in/out#

You can write data that contains category dtypes to a HDFStore.See here for an example and caveats.

It is also possible to write data to and reading data from Stata format files.See here for an example and caveats.

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

In [221]: import ioIn [222]: s = pd.Series(pd.Categorical(["a", "b", "b", "a", "a", "d"]))# rename the categoriesIn [223]: s = s.cat.rename_categories(["very good", "good", "bad"])# reorder the categories and add missing categoriesIn [224]: s = s.cat.set_categories(["very bad", "bad", "medium", "good", "very good"])In [225]: df = pd.DataFrame({"cats": s, "vals": [1, 2, 3, 4, 5, 6]})In [226]: csv = io.StringIO()In [227]: df.to_csv(csv)In [228]: df2 = pd.read_csv(io.StringIO(csv.getvalue()))In [229]: df2.dtypesOut[229]: Unnamed: 0 int64cats objectvals int64dtype: objectIn [230]: df2["cats"]Out[230]: 0 very good1 good2 good3 very good4 very good5 badName: cats, dtype: object# Redo the categoryIn [231]: df2["cats"] = df2["cats"].astype("category")In [232]: df2["cats"] = df2["cats"].cat.set_categories( .....:  ["very bad", "bad", "medium", "good", "very good"] .....: ) .....: In [233]: df2.dtypesOut[233]: Unnamed: 0 int64cats categoryvals int64dtype: objectIn [234]: df2["cats"]Out[234]: 0 very good1 good2 good3 very good4 very good5 badName: cats, dtype: categoryCategories (5, object): ['very bad', 'bad', 'medium', 'good', 'very good']

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 bydefault not included in computations. See the Missing Data section.

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 havea code of -1.

In [235]: s = pd.Series(["a", "b", np.nan, "a"], dtype="category")# only two categoriesIn [236]: sOut[236]: 0 a1 b2 NaN3 adtype: categoryCategories (2, object): ['a', 'b']In [237]: s.cat.codesOut[237]: 0 01 12 -13 0dtype: int8

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

In [238]: s = pd.Series(["a", "b", np.nan], dtype="category")In [239]: sOut[239]: 0 a1 b2 NaNdtype: categoryCategories (2, object): ['a', 'b']In [240]: pd.isna(s)Out[240]: 0 False1 False2 Truedtype: boolIn [241]: s.fillna("a")Out[241]: 0 a1 b2 adtype: categoryCategories (2, object): ['a', 'b']

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 anew categorical series will not remove unused categories but create a new categorical serieswhich is equal to the passed in one!
R allows for missing values to be included in its levels (pandas’ categories). pandasdoes 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.

In [242]: s = pd.Series(["foo", "bar"] * 1000)# object dtypeIn [243]: s.nbytesOut[243]: 16000# category dtypeIn [244]: s.astype("category").nbytesOut[244]: 2016

Note

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

In [245]: s = pd.Series(["foo%04d" % i for i in range(2000)])# object dtypeIn [246]: s.nbytesOut[246]: 16000# category dtypeIn [247]: s.astype("category").nbytesOut[247]: 20000

`Categorical` is not a `numpy` array#

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

NumPy itself doesn’t know about the new dtype:

In [248]: try: .....:  np.dtype("category") .....: except TypeError as e: .....:  print("TypeError:", str(e)) .....: TypeError: data type 'category' not understoodIn [249]: dtype = pd.Categorical(["a"]).dtypeIn [250]: try: .....:  np.dtype(dtype) .....: except TypeError as e: .....:  print("TypeError:", str(e)) .....: TypeError: Cannot interpret 'CategoricalDtype(categories=['a'], ordered=False, categories_dtype=object)' as a data type

Dtype comparisons work:

In [251]: dtype == np.str_Out[251]: FalseIn [252]: np.str_ == dtypeOut[252]: False

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

In [253]: hasattr(pd.Series(["a"], dtype="category"), "cat")Out[253]: TrueIn [254]: hasattr(pd.Series(["a"]), "cat")Out[254]: False

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

In [255]: s = pd.Series(pd.Categorical([1, 2, 3, 4]))In [256]: try: .....:  np.sum(s) .....: except TypeError as e: .....:  print("TypeError:", str(e)) .....: TypeError: 'Categorical' with dtype category does not support reduction 'sum'

Note

If such a function works, please file a bug at pandas-dev/pandas!

dtype in apply#

pandas currently does not preserve the dtype in apply functions: If you apply along rows you geta Series of object dtype (same as getting a row -> getting one element will return abasic type) and applying along columns will also convert to object. NaN values are unaffected.You can use fillna to handle missing values before applying a function.

In [257]: df = pd.DataFrame( .....:  { .....:  "a": [1, 2, 3, 4], .....:  "b": ["a", "b", "c", "d"], .....:  "cats": pd.Categorical([1, 2, 3, 2]), .....:  } .....: ) .....: In [258]: df.apply(lambda row: type(row["cats"]), axis=1)Out[258]: 0 <class 'int'>1 <class 'int'>2 <class 'int'>3 <class 'int'>dtype: objectIn [259]: df.apply(lambda col: col.dtype, axis=0)Out[259]: a int64b objectcats categorydtype: object

Categorical index#

CategoricalIndex is a type of index that is useful for supportingindexing with duplicates. This is a container around a Categoricaland allows efficient indexing and storage of an index with a large number of duplicated elements.See the advanced indexing docs for a more detailedexplanation.

Setting the index will create a CategoricalIndex:

In [260]: cats = pd.Categorical([1, 2, 3, 4], categories=[4, 2, 3, 1])In [261]: strings = ["a", "b", "c", "d"]In [262]: values = [4, 2, 3, 1]In [263]: df = pd.DataFrame({"strings": strings, "values": values}, index=cats)In [264]: df.indexOut[264]: CategoricalIndex([1, 2, 3, 4], categories=[4, 2, 3, 1], ordered=False, dtype='category')# This now sorts by the categories orderIn [265]: df.sort_index()Out[265]:  strings values4 d 12 b 23 c 31 a 4

Side effects#

Constructing a Series from a Categorical will not copy the inputCategorical. This means that changes to the Series will in most caseschange the original Categorical:

In [266]: cat = pd.Categorical([1, 2, 3, 10], categories=[1, 2, 3, 4, 10])In [267]: s = pd.Series(cat, name="cat")In [268]: catOut[268]: [1, 2, 3, 10]Categories (5, int64): [1, 2, 3, 4, 10]In [269]: s.iloc[0:2] = 10In [270]: catOut[270]: [10, 10, 3, 10]Categories (5, int64): [1, 2, 3, 4, 10]

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

In [271]: cat = pd.Categorical([1, 2, 3, 10], categories=[1, 2, 3, 4, 10])In [272]: s = pd.Series(cat, name="cat", copy=True)In [273]: catOut[273]: [1, 2, 3, 10]Categories (5, int64): [1, 2, 3, 4, 10]In [274]: s.iloc[0:2] = 10In [275]: catOut[275]: [1, 2, 3, 10]Categories (5, int64): [1, 2, 3, 4, 10]

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 behavior, while usinga string array (e.g. np.array(["a","b","c","a"])) will not.

Categorical data — pandas 2.2.3 documentation (2024)

Object creation#

Series creation#

DataFrame creation#

Controlling behavior#

Regaining original data#

CategoricalDtype#

Equality semantics#

Description#

Working with categories#

Renaming categories#

Appending new categories#

Removing categories#

Removing unused categories#

Setting categories#

Sorting and order#

Reordering#

Multi column sorting#

Comparisons#

Operations#

Data munging#

Getting#

String and datetime accessors#

Setting#

Merging / concatenation#

Unioning#

Getting data in/out#

Missing data#

Differences to R’s factor#

Gotchas#

Memory usage#

Categorical is not a numpy array#

dtype in apply#

Categorical index#

Side effects#

References

Differences to R’s `factor`#

`Categorical` is not a `numpy` array#