It is byĭefault not included in computations. Pandas primarily uses the value np.nan to represent missing data. The same holds for writing to a SQL database with to_sql. dtypes Out: Unnamed: 0 int64 cats category vals int64 dtype: object In : df2 Out: 0 very good 1 good 2 good 3 very good 4 very good 5 bad Name: cats, dtype: category Categories (5, object): dtypes Out: Unnamed: 0 int64 cats object vals int64 dtype: object In : df2 Out: 0 very good 1 good 2 good 3 very good 4 very good 5 bad Name: cats, dtype: object # Redo the category In : df2 = df2. rename_categories () # reorder the categories and add missing categories In : s = s. ![]() Categorical ()) # rename the categories In : s = s. Relevant columns back to category and assign the right categories and categories ordering. So if you read back the CSV file you have to convert the Writing to a CSV file will convert the data, effectively removing any information about theĬategorical (categories and ordering). It is also possible to write data to and reading data from Stata format files. You can write data that contains category dtypes to a HDFStore. codes Out: array(, dtype=int8) Getting data in/out # codes Out: array(, dtype=int8) In : c = union_categoricals () In : c Out: Categories (3, object): # "b" is coded to 0 throughout, same as c1, different from c2 In : c. codes Out: array(, dtype=int8) In : c2 Out: Categories (2, object): # "b" is coded to 1 In : c2. ![]() Categorical () In : c1 Out: Categories (2, object): # "b" is coded to 0 In : c1. If you want to combine categoricals that do not necessarily have the sameĬategories, the union_categoricals() function willĬombine a list-like of categoricals. See also the section on merge dtypes for notes about ![]() The following table summarizes the results of merging Categoricals: astype ( "category" ) Out: 0 a 1 b 0 b 1 c dtype: category Categories (3, object): In : union_categoricals () Out: Categories (3, object): Series (, dtype = "category" ) In : float_cats = pd. concat () Out: 0 a 1 b 0 b 1 c dtype: object # Output dtype is inferred based on categories values In : int_cats = pd. concat () Out: 0 a 1 b 0 a 1 b 2 a dtype: category Categories (2, object): # different categories In : s3 = pd. Series (, dtype = "category" ) In : s2 = pd. In : from import union_categoricals # same categories In : s1 = pd. Object creation # Series creation #Ĭategorical Series or columns in a DataFrame can be created in several ways:īy specifying dtype="category" when constructing a Series: to use suitable statistical methods or plot types). Min/max will use the logical order instead of the lexical order, see here.Īs a signal to other Python libraries that this column should be treated as a categorical ![]() The lexical order of a variable is not the same as the logical order (“one”, “two”, “three”).īy converting to a categorical and specifying an order on the categories, sorting and Variable to a categorical variable will save some memory, see here. The categorical data type is useful in the following cases:Ī string variable consisting of only a few different values. Internally, the data structureĬonsists of a categories array and an integer array of codes which point to the real value in The order of categories, not lexical order of the values. Operations (additions, divisions, …) are not possible.Īll values of categorical data are either in categories or np.nan. ‘strongly agree’ vs ‘agree’ or ‘first observation’ vs. In contrast to statistical categorical variables, categorical data might have an order (e.g. Social class, blood type, country affiliation, observation time or rating via Number of possible values ( categories levels in R). A categorical variable takes on a limited, and usually fixed, This is an introduction to pandas categorical data type, including a short comparisonĬategoricals are a pandas data type corresponding to categorical variables in
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