Python数据分析与可视化自行车租赁统计数据分析（综合实训）
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自行车租赁统计数据分析
实训：自行车租赁统计数据分析
import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize = (10,8))
%matplotlib inline
bike = pd.read_csv('data//bike.csv')
bike.head()
| datetime | season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011-01-01 00:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 81 | 0.0 | 3 | 13 | 16 |
| 1 | 2011-01-01 01:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 8 | 32 | 40 |
| 2 | 2011-01-01 02:00:00 | 1 | 0 | 0 | 1 | 9.02 | 13.635 | 80 | 0.0 | 5 | 27 | 32 |
| 3 | 2011-01-01 03:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 3 | 10 | 13 |
| 4 | 2011-01-01 04:00:00 | 1 | 0 | 0 | 1 | 9.84 | 14.395 | 75 | 0.0 | 0 | 1 | 1 |
查看有无缺失值
bike.isnull().sum()
datetime 0
season 0
holiday 0
workingday 0
weather 0
temp 0
atemp 0
humidity 0
windspeed 0
casual 0
registered 0
count 0
dtype: int64
查看待处理数据的数据类型
bike.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
datetime 10886 non-null object
season 10886 non-null int64
holiday 10886 non-null int64
workingday 10886 non-null int64
weather 10886 non-null int64
temp 10886 non-null float64
atemp 10886 non-null float64
humidity 10886 non-null int64
windspeed 10886 non-null float64
casual 10886 non-null int64
registered 10886 non-null int64
count 10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.6+ KB
bike.datetime = pd.to_datetime(bike.datetime)
bike.dtypes
datetime datetime64[ns]
season int64
holiday int64
workingday int64
weather int64
temp float64
atemp float64
humidity int64
windspeed float64
casual int64
registered int64
count int64
dtype: object
bike = bike.set_index('datetime')
先从数值型数据入手，可以看出租赁额（count）数值差异大，所以希望观察一下它们的密度分布
sns.distplot(s, bins = 10, hist = True, kde = True,
norm_hist = False, rug = True, vertical = False,
color = ‘g', label = 'distplot', axlabel = 'x')
sns.distplot(bike["count"])
# plt.plot(s.index,s.values) #作图
bike["count"].describe()
count 10886.000000
mean 191.574132
std 181.144454
min 1.000000
25% 42.000000
50% 145.000000
75% 284.000000
max 977.000000
Name: count, dtype: float64
def Count(x):
if x <42:
return np.nan
else:
return x
bike1 = bike
bike1["count"] = bike1["count"].apply(Count)
bike1 = bike1.dropna(axis=0, how='any')
sns.distplot(bike1["count"])
def Count(x):
if x <145:
return np.nan
else:
return x
bike2= bike
bike2["count"] = bike2["count"].apply(Count)
bike2 = bike2.dropna(axis=0, how='any')
sns.distplot(bike2["count"])
# plt.plot(s.index,s.values)#作图
bike = bike2
bike.shape
(5455, 11)
y_bike = bike.groupby(bike.index.year).mean()['count']
y_bike
datetime
2011 274.526697
2012 366.408629
Name: count, dtype: float64
y_bike.plot(kind='bar',rot = 0)
mm_bike = bike.resample('M',kind = "period").mean()
mm_bike.head()
| season | holiday | workingday | weather | temp | atemp | humidity | windspeed | casual | registered | count | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| datetime | |||||||||||
| 2011-01 | 1.0 | 0.000000 | 1.000000 | 1.160000 | 8.692000 | 10.909600 | 49.320000 | 11.880440 | 5.280000 | 175.520000 | 180.800000 |
| 2011-02 | 1.0 | 0.000000 | 0.791045 | 1.283582 | 14.294925 | 17.243134 | 44.179104 | 18.179100 | 23.835821 | 168.208955 | 192.044776 |
| 2011-03 | 1.0 | 0.000000 | 0.666667 | 1.291667 | 16.553750 | 19.728021 | 49.458333 | 18.187778 | 48.583333 | 163.781250 | 212.364583 |
| 2011-04 | 2.0 | 0.078014 | 0.617021 | 1.453901 | 19.970780 | 23.634752 | 55.177305 | 16.893741 | 60.624113 | 177.539007 | 238.163121 |
| 2011-05 | 2.0 | 0.000000 | 0.758197 | 1.446721 | 23.060820 | 27.214037 | 64.069672 | 13.946627 | 55.745902 | 224.110656 | 279.856557 |
mm_bike.plot()
plt.legend(loc = "best",fontsize = 8)
m_bike = bike.groupby(bike.index.month).mean()['count']
m_bike
datetime
1 246.528736
2 250.560784
3 301.152738
4 336.355450
5 336.163265
6 355.171329
7 339.714533
8 343.414035
9 363.035849
10 354.840304
11 308.497992
12 300.295045
Name: count, dtype: float64
m_bike.plot()
plt.grid()# 9月份达到最大值
h_bike = bike.groupby(bike.index.hour).mean()['count']
h_bike
datetime
0 175.040000
1 159.666667
6 170.777778
7 332.275194
8 464.194611
9 256.773109
10 247.983051
11 273.677966
12 300.908587
13 306.273504
14 294.686217
15 302.322946
16 358.840314
17 493.831382
18 462.374101
19 361.031746
20 280.290419
21 227.334507
22 197.688776
23 180.289855
Name: count, dtype: float64
h_bike.plot("bar",rot = 0)
season_bike = bike.groupby(bike.season).mean()['count']
season_bike
season
1 272.279639
2 343.308545
3 348.337306
4 322.621935
Name: count, dtype: float64
season_bike.plot(kind = "bar",rot = 0)
temp_bike = bike.groupby([bike.temp]).mean()['count']
temp_bike.sample(10)
temp
27.06 346.248826
34.44 351.315789
36.90 347.000000
16.40 299.202128
4.92 220.857143
24.60 350.036885
11.48 256.326923
22.96 336.873303
1.64 180.000000
26.24 365.153257
Name: count, dtype: float64
temp_bike.plot()
wind_bike = bike.groupby(bike.windspeed).mean()['count']
wind_bike.sort_values(ascending=False).sample(10)
windspeed
43.9989 389.333333
40.9973 229.375000
32.9975 293.097561
39.0007 335.636364
26.0027 356.098485
47.9988 280.000000
22.0028 319.870056
11.0014 336.831826
7.0015 325.181614
15.0013 340.349904
Name: count, dtype: float64
wind_bike_sort = wind_bike.sort_values(ascending=False)
wind_bike_sort.head(20).plot(kind="bar",rot = 60)
weather_bike = bike.groupby(bike.weather).mean()['count']
weather_bike.plot(kind='bar',rot = 0)
加油!
感谢!
努力!
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