利用Pandas探究自行车租赁随时间及天气变化的分布情况并可视化（附源码 超详细）

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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

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bike = pd.read_csv('data//bike.csv')
bike.head()

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bike.isnull().sum()

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bike.info()

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bike.datetime = pd.to_datetime(bike.datetime)
bike.dtypes

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sns.distplot(bike["count"])
# plt.plot(s.index,s.values)#作图

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bike["count"].describe()

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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"])

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y_bike.plot(kind='bar',rot = 0)

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mm_bike = bike.resample('M',kind = "period").mean()
mm_bike.head()

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mm_bike.plot()
plt.legend(loc = "best",fontsize = 8)

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m_bike.plot()
plt.grid()# 9月份达到最大值

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h_bike.plot("bar",rot = 0)

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season_bike = bike.groupby(bike.season).mean()['count']
season_bike 
season_bike.plot(kind = "bar",rot = 0)

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temp_bike = bike.groupby([bike.temp]).mean()['count']
temp_bike.sample(10)
temp_bike.plot()

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wind_bike_sort = wind_bike.sort_values(ascending=False)
wind_bike_sort.head(20).plot(kind="bar",rot = 60)

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weather_bike = bike.groupby(bike.weather).mean()['count']
weather_bike.plot(kind='bar',rot = 0)