Date categoriale în pandas

Lucrul cu date categoriale în Python

Kasey Jones

Research Data Scientist

dtype-uri: object

adult = pd.read_csv("data/adult.csv")
adult.dtypes

Age                 int64
Workclass          object
fnlgwt              int64
Education          object
Education Num       int64
Marital Status     object
Occupation         object
Relationship       object
...

dtype-uri: categorial

dtype implicit:

adult["Marital Status"].dtype

dtype('O')

Setare ca tip categorial:

adult["Marital Status"] = adult["Marital Status"].astype("category")

adult["Marital Status"].dtype

CategoricalDtype(categories=[' Divorced', ' Married-AF-spouse',
' Married-civ-spouse', ' Married-spouse-absent', ' Never-married',
' Separated', ' Widowed'], ordered=False)

Crearea unui Series categorial

my_data = ["A", "A", "C", "B", "C", "A"]

my_series1 = pd.Series(my_data, dtype="category")
print(my_series1)

0    A
1    A
2    C
...
dtype: category
Categories (3, object): [A, B, C]

Crearea unui Series categorial

my_data = ["A", "A", "C", "B", "C", "A"]
my_series2 = pd.Categorical(my_data, categories=["C", "B", "A"], ordered=True)
my_series2

[A, A, C, B, C, A]
Categories (3, object): [C < B < A]

De ce folosim tipul categorial: memorie

Economie de memorie:

adult = pd.read_csv("data/adult.csv")
adult["Marital Status"].nbytes

adult["Marital Status"] = adult["Marital Status"].astype("category")
adult["Marital Status"].nbytes

Specificați dtype-urile la citirea datelor

Creați un dicționar:

adult_dtypes = {"Marital Status": "category"}

Setați parametrul dtype:

adult = pd.read_csv("data/adult.csv", dtype=adult_dtypes)

Verificați dtype:
```
adult["Marital Status"].dtype
```

CategoricalDtype(categories=[' Divorced', ' Married-AF-spouse',
                             ..., ' Widowed'], ordered=False)

Practică pandas category

Lucrul cu date categoriale în Python