[Pandas] 데이터 살펴보기 / head,tail,info,describe,shape,count,value_counts

 

 

 

데이터 살펴보기¶

head(n) : 처음 n개의 행을 보여줌 / tail(n) : 마지막 n개의 행을 보여줌¶

In [1]:

import pandas as pd

df = pd.read_csv('C:\\Users\\rladl\\Jupyter.study\\05000266\\part3\\auto-mpg.csv', header=None)

df.columns = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model year','origin','name']

print(df.head())
print('\n')
print(df.tail())

 

    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \
0  18.0          8         307.0      130.0  3504.0          12.0          70   
1  15.0          8         350.0      165.0  3693.0          11.5          70   
2  18.0          8         318.0      150.0  3436.0          11.0          70   
3  16.0          8         304.0      150.0  3433.0          12.0          70   
4  17.0          8         302.0      140.0  3449.0          10.5          70   

   origin                       name  
0       1  chevrolet chevelle malibu  
1       1          buick skylark 320  
2       1         plymouth satellite  
3       1              amc rebel sst  
4       1                ford torino  


      mpg  cylinders  displacement horsepower  weight  acceleration  \
393  27.0          4         140.0      86.00  2790.0          15.6   
394  44.0          4          97.0      52.00  2130.0          24.6   
395  32.0          4         135.0      84.00  2295.0          11.6   
396  28.0          4         120.0      79.00  2625.0          18.6   
397  31.0          4         119.0      82.00  2720.0          19.4   

     model year  origin             name  
393          82       1  ford mustang gl  
394          82       2        vw pickup  
395          82       1    dodge rampage  
396          82       1      ford ranger  
397          82       1       chevy s-10  

 

데이터 요약 정보 확인하기¶

In [2]:

# df의 모양과 크기 확인 : (행의 개수, 열의 개수)를 튜플로 반환
print(df.shape)

 

(398, 9)

In [3]:

# 데이터 프레임의 df의 내용 확인
print(df.info())

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   mpg           398 non-null    float64
 1   cylinders     398 non-null    int64  
 2   displacement  398 non-null    float64
 3   horsepower    398 non-null    object 
 4   weight        398 non-null    float64
 5   acceleration  398 non-null    float64
 6   model year    398 non-null    int64  
 7   origin        398 non-null    int64  
 8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB
None

In [4]:

# 데이터 프레임 df의 자료형 확인
print(df.dtypes)
print('\n')

print(df.mpg.dtypes)

 

mpg             float64
cylinders         int64
displacement    float64
horsepower       object
weight          float64
acceleration    float64
model year        int64
origin            int64
name             object
dtype: object


float64

 

shape() 데이터프레임의 (행,열) 크기 확인 가능¶

info() 데이터프레임의 관한 기본 정보 출력 (클래스 유형, 행 인덱스의 구성, 열 이름의 종류와 개수, 각 열의 자료형과 개수, 메모리 할당량)¶

dtypes() 각 열의 자료형 확인¶

---

 

데이터프레임의 기술 통계 정보 요약¶

describe() 산술 데이터를 갖는 열에 대한 주요 기술 통계 정보(평균, 표준편차, 최대값, 최소값, 중간값)을 요약하여 출력¶

In [6]:

print(df.describe)
print('\n')
print(df.describe(include='all'))
# unique : 고유값 개수 top : 최빈값 freq : 빈도수 

 

<bound method NDFrame.describe of       mpg  cylinders  displacement horsepower  weight  acceleration  \
0    18.0          8         307.0      130.0  3504.0          12.0   
1    15.0          8         350.0      165.0  3693.0          11.5   
2    18.0          8         318.0      150.0  3436.0          11.0   
3    16.0          8         304.0      150.0  3433.0          12.0   
4    17.0          8         302.0      140.0  3449.0          10.5   
..    ...        ...           ...        ...     ...           ...   
393  27.0          4         140.0      86.00  2790.0          15.6   
394  44.0          4          97.0      52.00  2130.0          24.6   
395  32.0          4         135.0      84.00  2295.0          11.6   
396  28.0          4         120.0      79.00  2625.0          18.6   
397  31.0          4         119.0      82.00  2720.0          19.4   

     model year  origin                       name  
0            70       1  chevrolet chevelle malibu  
1            70       1          buick skylark 320  
2            70       1         plymouth satellite  
3            70       1              amc rebel sst  
4            70       1                ford torino  
..          ...     ...                        ...  
393          82       1            ford mustang gl  
394          82       2                  vw pickup  
395          82       1              dodge rampage  
396          82       1                ford ranger  
397          82       1                 chevy s-10  

[398 rows x 9 columns]>


               mpg   cylinders  displacement horsepower       weight  \
count   398.000000  398.000000    398.000000        398   398.000000   
unique         NaN         NaN           NaN         94          NaN   
top            NaN         NaN           NaN      150.0          NaN   
freq           NaN         NaN           NaN         22          NaN   
mean     23.514573    5.454774    193.425879        NaN  2970.424623   
std       7.815984    1.701004    104.269838        NaN   846.841774   
min       9.000000    3.000000     68.000000        NaN  1613.000000   
25%      17.500000    4.000000    104.250000        NaN  2223.750000   
50%      23.000000    4.000000    148.500000        NaN  2803.500000   
75%      29.000000    8.000000    262.000000        NaN  3608.000000   
max      46.600000    8.000000    455.000000        NaN  5140.000000   

        acceleration  model year      origin        name  
count     398.000000  398.000000  398.000000         398  
unique           NaN         NaN         NaN         305  
top              NaN         NaN         NaN  ford pinto  
freq             NaN         NaN         NaN           6  
mean       15.568090   76.010050    1.572864         NaN  
std         2.757689    3.697627    0.802055         NaN  
min         8.000000   70.000000    1.000000         NaN  
25%        13.825000   73.000000    1.000000         NaN  
50%        15.500000   76.000000    1.000000         NaN  
75%        17.175000   79.000000    2.000000         NaN  
max        24.800000   82.000000    3.000000         NaN  

 

데이터 개수 확인¶

In [7]:

print(df.count())
print('\n')
print(type(df.count()))

 

mpg             398
cylinders       398
displacement    398
horsepower      398
weight          398
acceleration    398
model year      398
origin          398
name            398
dtype: int64


<class 'pandas.core.series.Series'>

 

각 열의 고유값 개수¶

value_counts() 시리즈 객체의 고유값 개수를 세는데 사용¶

In [8]:

unique_values = df['origin'].value_counts()
print(unique_values)
print('\n')

print(type(unique_values))

 

1    249
3     79
2     70
Name: origin, dtype: int64


<class 'pandas.core.series.Series'>

In [10]:

# 평균값
print(df.mean())
print('\n')
print(df['mpg'].mean())
print('\n')
print(df[['mpg','weight']].mean())

 

mpg               23.514573
cylinders          5.454774
displacement     193.425879
weight          2970.424623
acceleration      15.568090
model year        76.010050
origin             1.572864
dtype: float64


23.514572864321615


mpg         23.514573
weight    2970.424623
dtype: float64

 

C:\Users\rladl\AppData\Local\Temp/ipykernel_14012/2164482834.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.mean())

In [11]:

# 중간값
print(df.median())
print('\n')
print(df['mpg'].median())

 

mpg               23.0
cylinders          4.0
displacement     148.5
weight          2803.5
acceleration      15.5
model year        76.0
origin             1.0
dtype: float64


23.0

 

C:\Users\rladl\AppData\Local\Temp/ipykernel_14012/28750081.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.median())

In [12]:

# 최대값
print(df.max())
print('\n')

# 최소값
print(df.min())

 

mpg                         46.6
cylinders                      8
displacement               455.0
horsepower                     ?
weight                    5140.0
acceleration                24.8
model year                    82
origin                         3
name            vw rabbit custom
dtype: object


mpg                                 9.0
cylinders                             3
displacement                       68.0
horsepower                        100.0
weight                           1613.0
acceleration                        8.0
model year                           70
origin                                1
name            amc ambassador brougham
dtype: object

In [13]:

# 표준편차
print(df.std())
print('\n')

# 상관계수
print(df.corr())

 

mpg               7.815984
cylinders         1.701004
displacement    104.269838
weight          846.841774
acceleration      2.757689
model year        3.697627
origin            0.802055
dtype: float64


                   mpg  cylinders  displacement    weight  acceleration  \
mpg           1.000000  -0.775396     -0.804203 -0.831741      0.420289   
cylinders    -0.775396   1.000000      0.950721  0.896017     -0.505419   
displacement -0.804203   0.950721      1.000000  0.932824     -0.543684   
weight       -0.831741   0.896017      0.932824  1.000000     -0.417457   
acceleration  0.420289  -0.505419     -0.543684 -0.417457      1.000000   
model year    0.579267  -0.348746     -0.370164 -0.306564      0.288137   
origin        0.563450  -0.562543     -0.609409 -0.581024      0.205873   

              model year    origin  
mpg             0.579267  0.563450  
cylinders      -0.348746 -0.562543  
displacement   -0.370164 -0.609409  
weight         -0.306564 -0.581024  
acceleration    0.288137  0.205873  
model year      1.000000  0.180662  
origin          0.180662  1.000000  

 

C:\Users\rladl\AppData\Local\Temp/ipykernel_14012/2183833806.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.std())

 

---

판다스 내장 그래프 도구 활용¶

In [2]:

df = pd.read_excel('C:\\Users\\rladl\\Jupyter.study\\05000266\part3\\남북한발전전력량.xlsx',engine='openpyxl')

df_ns = df.iloc[[0,5],3:] # 남한, 북한 발전량 합계 데이터만 추출
df_ns.index = ['South','North'] # 행 인덱스 변경
df_ns.columns = df_ns.columns.map(int) # 열 이름의 자료형을 정수형으로 변경
print(df_ns.head())

 

       1991  1992  1993  1994  1995  1996  1997  1998  1999  2000  ...  2007  \
South  1186  1310  1444  1650  1847  2055  2244  2153  2393  2664  ...  4031   
North   263   247   221   231   230   213   193   170   186   194  ...   236   

       2008  2009  2010  2011  2012  2013  2014  2015  2016  
South  4224  4336  4747  4969  5096  5171  5220  5281  5404  
North   255   235   237   211   215   221   216   190   239  

[2 rows x 26 columns]

In [17]:

tdf_ns = df_ns.T
print(tdf_ns.head())
print('\n')
tdf_ns.plot()

 

     South North
1991  1186   263
1992  1310   247
1993  1444   221
1994  1650   231
1995  1847   230

Out[17]:

<AxesSubplot:>

 

In [19]:

print(tdf_ns.head())
print('\n')
tdf_ns.plot(kind='bar')

 

     South North
1991  1186   263
1992  1310   247
1993  1444   221
1994  1650   231
1995  1847   230

Out[19]:

<AxesSubplot:>

 

In [20]:

tdf_ns.plot(kind='hist')

Out[20]:

<AxesSubplot:ylabel='Frequency'>

 

In [21]:

df = pd.read_csv('C:\\Users\\rladl\\Jupyter.study\\05000266\\part3\\auto-mpg.csv')
df.columns = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model year','origin','name']
df.plot(x='weight',y='mpg',kind='scatter')

Out[21]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

 

In [22]:

df[['mpg','cylinders']].plot(kind='box')

Out[22]:

<AxesSubplot:>

 

 

정리 : 데이터프레임(시리즈) 객체 그래프 그리기¶

plot() -> 선 그래프¶

옵션 : kind='bar' (막대 G) , kind='hist' (히스토그램) , kind='scatter' (산점도 / x,y에 열 선택), kind='box' (박스 플롯 / x,y에 열 선택)¶