판다스 기초 :: Python 빅데이터 처리와 시각화 - mindscale
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판다스 기초

import pandas as pd

엑셀 파일 열기

df = pd.read_excel('census.xlsx')

첫 부분 보기

df.head()
age workclass fnlwgt education education_num marital_status occupation relationship race sex capital_gain capital_loss hours_per_week native_country income
0 39 State-gov 77516 Bachelors 13 Never-married Adm-clerical Not-in-family White Male 2174 0 40 United-States <=50K
1 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 0 0 13 United-States <=50K
2 38 Private 215646 HS-grad 9 Divorced Handlers-cleaners Not-in-family White Male 0 0 40 United-States <=50K
3 53 Private 234721 11th 7 Married-civ-spouse Handlers-cleaners Husband Black Male 0 0 40 United-States <=50K
4 28 Private 338409 Bachelors 13 Married-civ-spouse Prof-specialty Wife Black Female 0 0 40 Cuba <=50K

csv로 저장, 엑셀로 저장 csv 열기

컬럼 이름

df.columns

Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',
       'marital_status', 'occupation', 'relationship', 'race', 'sex',
       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',
       'income'],
      dtype='object')

컬럼 선택

df['age']

0        39
1        50
2        38
         ..
32558    58
32559    22
32560    52
Name: age, Length: 32561, dtype: int64

dtypes와 dtype

df.dtypes

age                int64
workclass         object
fnlwgt             int64
                   ...  
hours_per_week     int64
native_country    object
income            object
Length: 15, dtype: object
df['age'].dtype

dtype('int64')

배열로 변환

df['age'].to_numpy()  # 주의: 여기까지는 import numpy 필요 없음

array([39, 50, 38, ..., 58, 22, 52], dtype=int64)

통계 계산

import numpy as np 

np.max(df['age'])

90
df['age'].max()

90
2019 - df['age']

0        1980
1        1969
2        1981
         ... 
32558    1961
32559    1997
32560    1967
Name: age, Length: 32561, dtype: int64

여러 컬럼에 계산

cols = ['age', 'education_num']

df[cols].head()
age education_num
0 39 13
1 50 13
2 38 9
3 53 7
4 28 13 
df[cols].max()

age              90
education_num    16
dtype: int64
df[cols].agg(np.max)

age              90
education_num    16
dtype: int64

정렬

df.sort_values('age').head()
age workclass fnlwgt education education_num marital_status occupation relationship race sex capital_gain capital_loss hours_per_week native_country income
12318 17 Private 127366 11th 7 Never-married Sales Own-child White Female 0 0 8 United-States <=50K
6312 17 Private 132755 11th 7 Never-married Sales Own-child White Male 0 0 15 United-States <=50K
30927 17 Private 108470 11th 7 Never-married Other-service Own-child Black Male 0 0 17 United-States <=50K
12787 17 Local-gov 308901 11th 7 Never-married Adm-clerical Own-child White Female 0 0 15 United-States <=50K
25755 17 ? 47407 11th 7 Never-married ? Own-child White Male 0 0 10 United-States <=50K 
df.sort_values('age', ascending=False).head()  # 내림차순
age workclass fnlwgt education education_num marital_status occupation relationship race sex capital_gain capital_loss hours_per_week native_country income
5406 90 Private 51744 Masters 14 Never-married Exec-managerial Not-in-family Black Male 0 0 50 United-States >50K
6624 90 Private 313986 11th 7 Married-civ-spouse Craft-repair Husband White Male 0 0 40 United-States <=50K
20610 90 Private 206667 Masters 14 Married-civ-spouse Prof-specialty Wife White Female 0 0 40 United-States >50K
1040 90 Private 137018 HS-grad 9 Never-married Other-service Not-in-family White Female 0 0 40 United-States <=50K
1935 90 Private 221832 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 0 0 45 United-States <=50K

쿼리

df.query('age < 18 and capital_gain > 1000')
age workclass fnlwgt education education_num marital_status occupation relationship race sex capital_gain capital_loss hours_per_week native_country income
106 17 ? 304873 10th 6 Never-married ? Own-child White Female 34095 0 32 United-States <=50K
271 17 Private 191260 9th 5 Never-married Other-service Own-child White Male 1055 0 24 United-States <=50K
421 17 Private 175024 11th 7 Never-married Handlers-cleaners Own-child White Male 2176 0 18 United-States <=50K
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1691 17 Private 103851 11th 7 Never-married Adm-clerical Own-child White Female 1055 0 20 United-States <=50K
3605 17 Private 130125 10th 6 Never-married Other-service Own-child Amer-Indian-Eskimo Female 1055 0 20 United-States <=50K
27889 17 Private 56536 11th 7 Never-married Sales Own-child White Female 1055 0 18 India <=50K

7 rows × 15 columns

그룹

df.groupby('income').agg({'education_num': np.mean})
education_num
income
<=50K 9.595065
>50K 11.611657

상자-수염 그림

import seaborn as sns

sns.boxplot(x="income", y="education_num", data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x218f61f7978>