
import pandas as pd


train = pd.read_csv("bike/train.csv", parse_dates=['datetime'])
test = pd.read_csv("bike/test.csv", parse_dates=['datetime'])


train.columns

Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',
       'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'count'],
      dtype='object')


test.columns

Index(['datetime', 'season', 'holiday', 'workingday', 'weather', 'temp',
       'atemp', 'humidity', 'windspeed'],
      dtype='object')


train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   datetime    10886 non-null  datetime64[ns]
 1   season      10886 non-null  int64         
 2   holiday     10886 non-null  int64         
 3   workingday  10886 non-null  int64         
 4   weather     10886 non-null  int64         
 5   temp        10886 non-null  float64       
 6   atemp       10886 non-null  float64       
 7   humidity    10886 non-null  int64         
 8   windspeed   10886 non-null  float64       
 9   casual      10886 non-null  int64         
 10  registered  10886 non-null  int64         
 11  count       10886 non-null  int64         
dtypes: datetime64[ns](1), float64(3), int64(8)
memory usage: 1020.7 KB


test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6493 entries, 0 to 6492
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype         
---  ------      --------------  -----         
 0   datetime    6493 non-null   datetime64[ns]
 1   season      6493 non-null   int64         
 2   holiday     6493 non-null   int64         
 3   workingday  6493 non-null   int64         
 4   weather     6493 non-null   int64         
 5   temp        6493 non-null   float64       
 6   atemp       6493 non-null   float64       
 7   humidity    6493 non-null   int64         
 8   windspeed   6493 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(5)
memory usage: 456.7 KB


import matplotlib.pyplot as plt
import matplotlib


train.datetime.describe()

<ipython-input-13-120836598240>:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.
  train.datetime.describe()

count                   10886
unique                  10886
top       2011-06-09 04:00:00
freq                        1
first     2011-01-01 00:00:00
last      2012-12-19 23:00:00
Name: datetime, dtype: object


plt.plot(train['count'], train['temp'], 'o', alpha=0.2)

[<matplotlib.lines.Line2D at 0x27bcac1db20>]


train.corr()['count']

season        0.163439
holiday      -0.005393
workingday    0.011594
weather      -0.128655
temp          0.394454
atemp         0.389784
humidity     -0.317371
windspeed     0.101369
casual        0.690414
registered    0.970948
count         1.000000
Name: count, dtype: float64


train.corr()['count'].abs().sort_values(ascending=False)

count         1.000000
registered    0.970948
casual        0.690414
temp          0.394454
atemp         0.389784
humidity      0.317371
season        0.163439
weather       0.128655
windspeed     0.101369
workingday    0.011594
holiday       0.005393
Name: count, dtype: float64


data = train.corr()['count'].abs().sort_values(ascending=True)
print(data.index)
print(data.values)

Index(['holiday', 'workingday', 'windspeed', 'weather', 'season', 'humidity',
       'atemp', 'temp', 'casual', 'registered', 'count'],
      dtype='object')
[0.00539298 0.01159387 0.10136947 0.1286552  0.16343902 0.31737148
 0.38978444 0.39445364 0.69041357 0.97094811 1.        ]


plt.barh(data.index, data.values)

<BarContainer object of 11 artists>


plt.barh(data.index, data.values)
plt.title("count corr values")
plt.xlabel("corr values")
plt.ylabel("features")

Text(0, 0.5, 'features')


train['season'].value_counts()

4    2734
3    2733
2    2733
1    2686
Name: season, dtype: int64


data = train['season'].value_counts()
plt.bar(data.index, data.values)

<BarContainer object of 4 artists>


plt.bar(data.index.astype(str), data.values)

<BarContainer object of 4 artists>


train['holiday'].value_counts()

0    10575
1      311
Name: holiday, dtype: int64


data = train['holiday'].value_counts()
plt.bar(data.index.astype(str), data.values)

<BarContainer object of 2 artists>


train['weather'].value_counts()

1    7192
2    2834
3     859
4       1
Name: weather, dtype: int64


data = train['weather'].value_counts()
plt.bar(data.index.astype(str), data.values)

<BarContainer object of 4 artists>


plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
plt.hist(train.temp)

plt.subplot(2,2,2)
plt.hist(train.atemp)

plt.subplot(2,2,3)
plt.hist(train.humidity)

plt.subplot(2,2,4)
plt.hist(train.windspeed)

(array([1.313e+03, 4.083e+03, 2.827e+03, 1.540e+03, 6.960e+02, 2.800e+02,
        1.070e+02, 3.100e+01, 6.000e+00, 3.000e+00]),
 array([ 0.     ,  5.69969, 11.39938, 17.09907, 22.79876, 28.49845,
        34.19814, 39.89783, 45.59752, 51.29721, 56.9969 ]),
 <BarContainer object of 10 artists>)


plt.figure(figsize=(15,10))
plt.hist(train.temp, alpha=0.3)
plt.hist(train.atemp, alpha=0.3)
plt.hist(train.humidity, alpha=0.3)
plt.hist(train.windspeed, alpha=0.3)

(array([1.313e+03, 4.083e+03, 2.827e+03, 1.540e+03, 6.960e+02, 2.800e+02,
        1.070e+02, 3.100e+01, 6.000e+00, 3.000e+00]),
 array([ 0.     ,  5.69969, 11.39938, 17.09907, 22.79876, 28.49845,
        34.19814, 39.89783, 45.59752, 51.29721, 56.9969 ]),
 <BarContainer object of 10 artists>)


from matplotlib import font_manager, rc
import matplotlib.pyplot as plt
import platform


path = "C:/Windows/Fonts/malgun.ttf"
if platform.system() == "Windows":
    font_name = font_manager.FontProperties(fname=path).get_name()
    rc('font', family=font_name)
elif platform.system()=="Darwin":
    rc('font', family='AppleGothic')
else:
    print("Unknown System")
    
matplotlib.rcParams['axes.unicode_minus'] = False


plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
plt.hist(train.temp)
plt.xlabel("temp", size=17)

plt.subplot(2,2,2)
plt.hist(train.atemp, color="#88c999")
plt.xlabel("atemp", size=17)

plt.subplot(2,2,3)
plt.hist(train.humidity, color='#B652BE')
plt.xlabel("humidity", size=17)

plt.subplot(2,2,4)
plt.hist(train.windspeed)
plt.xlabel("windspeed", size=17)

plt.suptitle("피처의 값의 분포", size=20)

Text(0.5, 0.98, '피처의 값의 분포')


print( train['weather'].count() )
all_cnt = train['weather'].count()
print( train['weather'].value_counts() / all_cnt )

10886
1    0.660665
2    0.260334
3    0.078909
4    0.000092
Name: weather, dtype: float64


plt.figure(figsize=(10,10))
dat = train['weather'].value_counts() / all_cnt
dat.index=['봄', '여름', '가을', '겨울']
plt.pie(dat.values, labels=dat.index)
plt.legend(title='계절')

<matplotlib.legend.Legend at 0x27bd09309a0>

Bike 데이터 셋을 활용한 데이터 처리 및 시각화¶

(실습1) 데이터를 알아가기 위한 여러가지 질문을 작성해 보자.¶

01. datetime은 언제부터 언제까지의 데이터일까?¶

02-A count와 temp는 어떤 상관관계가 있을까?¶

02-B corr()를 활용하여 count와 다른 feature(특징)간의 상관계수를 확인해 보자.¶

03. 계절별 데이터를 확인 및 시각화 해 보자.¶

04. 쉬는날과 아닌날의 데이터는 얼마나 될까? 이를 시각화하기¶

05. weather는 어떤 값을 갖고, 각각의 데이터의 수는 얼마나 될까?(시각화하기)¶

06. 아래의 값의 분포를 2행, 2열로 표시해 보자.¶

07. weather별 데이터의 비율은 어느정도 될까?¶

matplotlib을 사용하여 위의 문제를 풀어보자.¶

02-B corr()를 활용하여 count와 다른 feature(특징)간의 상관계수를 확인해 보자.¶

bar plot으로 그래프화 시켜보자.¶

03. 계절별 데이터를 확인 및 시각화 해 보자.¶

04. 쉬는날과 아닌날의 데이터는 얼마나 될까?¶

05. weather는 어떤 값을 갖고, 각각의 데이터의 수는 얼마나 될까?¶

06. 아래의 값의 분포를 2행, 2열로 표시해 보자.¶

07. weather별 데이터의 비율은 어느정도 될까?¶