자료형을 자유자재로 변환하기 - astype 메서드¶

In [1]:

import warnings
warnings.simplefilter(action="ignore", category = FutureWarning)

In [2]:

import pandas as pd
import seaborn as sns
tips = sns.load_dataset('tips')
tips

Out[2]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

In [3]:

tips.info

Out[3]:

<bound method DataFrame.info of      total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]>

여러가지 자료형을 문자열로 변환하기¶

In [4]:

# 자료형을 변혼하려면 astype 메서드 사용
# sex 열의 자료형은 카테고리 -> 문자열로 변환하여 
tips['sex_str'] = tips['sex'].astype(str)
tips.dtypes

Out[4]:

total_bill     float64
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

자료형을 변환한 데이터 다시 원래대로 만들기¶

In [5]:

# total_bill ==> 문자열 변환
tips['total_bill'] = tips['total_bill'].astype(str)
tips.dtypes

Out[5]:

total_bill      object
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

In [6]:

# 문자열로 변환한 total_bill 열을 다시 실수로 변환
tips['total_bill'] = tips['total_bill'].astype(float)
tips.dtypes

Out[6]:

total_bill     float64
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

잘못 입력한 문자열 처리하기 - to_numeric 메서드¶

In [7]:

# total_bill 열의 1, 3, 5, 7 행의 데이터를 'missing'으로 변경해서 tips_sub_miss에 저장
tips_sub_miss = tips.head(10)
tips_sub_miss.loc[[1, 3, 5, 7], 'total_bill'] = 'missing'
tips_sub_miss

Out[7]:

	total_bill	tip	sex	smoker	day	time	size	sex_str
0	16.99	1.01	Female	No	Sun	Dinner	2	Female
1	missing	1.66	Male	No	Sun	Dinner	3	Male
2	21.01	3.50	Male	No	Sun	Dinner	3	Male
3	missing	3.31	Male	No	Sun	Dinner	2	Male
4	24.59	3.61	Female	No	Sun	Dinner	4	Female
5	missing	4.71	Male	No	Sun	Dinner	4	Male
6	8.77	2.00	Male	No	Sun	Dinner	2	Male
7	missing	3.12	Male	No	Sun	Dinner	4	Male
8	15.04	1.96	Male	No	Sun	Dinner	2	Male
9	14.78	3.23	Male	No	Sun	Dinner	2	Male

In [8]:

# 'missing'이라는 문자열 때문에 실수가 아니라 문자열로 변경됨
tips_sub_miss.dtypes

Out[8]:

total_bill      object
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

In [9]:

##### pandas는 'missing'이라는 문자열을 실수로 변환하는 방법을 모름

# tips_sub_miss['total_bill'].astype(float)

##### output : error

In [10]:

#### to_numeric 메서드를사용해도 문자열을 실수로 변환할 수 없다

# pd.to_numeric(tips_sub_miss['total_bill'])

#### output : error

In [11]:

# errors 인자를 ignore로 설정하면 오류는 발생하지 않지만, 자료형도 변하지 않음

tips_sub_miss['total_bill'] = \
    pd.to_numeric(tips_sub_miss['total_bill'], errors='ignore')

tips_sub_miss.dtypes

C:\Users\Playdata\AppData\Local\Temp\ipykernel_5084\971638791.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tips_sub_miss['total_bill'] = \

Out[11]:

total_bill      object
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

In [12]:

# coerce: 숫자로 변환할 수 없는 값을 누락값으로 지정

tips_sub_miss['total_bill'] = \
    pd.to_numeric(tips_sub_miss['total_bill'], errors='coerce')

tips_sub_miss.dtypes

C:\Users\Playdata\AppData\Local\Temp\ipykernel_5084\584473275.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  tips_sub_miss['total_bill'] = \

Out[12]:

total_bill     float64
tip            float64
sex           category
smoker        category
day           category
time          category
size             int64
sex_str         object
dtype: object

In [13]:

tips_sub_miss.head()

Out[13]:

	total_bill	tip	sex	smoker	day	time	size	sex_str
0	16.99	1.01	Female	No	Sun	Dinner	2	Female
1	NaN	1.66	Male	No	Sun	Dinner	3	Male
2	21.01	3.50	Male	No	Sun	Dinner	3	Male
3	NaN	3.31	Male	No	Sun	Dinner	2	Male
4	24.59	3.61	Female	No	Sun	Dinner	4	Female

Woogi

[23.06.16] Python data_type - 11(3)

자료형을 자유자재로 변환하기 - astype 메서드¶

여러가지 자료형을 문자열로 변환하기¶

자료형을 변환한 데이터 다시 원래대로 만들기¶

잘못 입력한 문자열 처리하기 - to_numeric 메서드¶

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