1.911分类案例_代码示例

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import font_manager
my_font = font_manager.FontProperties(fname="c:/Windows/Fonts/simkai.ttf")df = pd.read_csv("./911.csv")
# print(df.info())
'''
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 249737 entries, 0 to 249736
Data columns (total 9 columns):#   Column     Non-Null Count   Dtype  
---  ------     --------------   -----  0   lat        249737 non-null  float641   lng        249737 non-null  float642   desc       249737 non-null  object 3   zip        219391 non-null  float644   title      249737 non-null  object 5   timeStamp  249737 non-null  object 6   twp        249644 non-null  object 7   addr       249737 non-null  object 8   e          249737 non-null  int64  
dtypes: float64(3), int64(1), object(5)
memory usage: 17.1+ MB
'''time_data = pd.DataFrame(df['timeStamp'])
# print(time_data)
'''timeStamp
0       2015-12-10 17:10:52
1       2015-12-10 17:29:21
2       2015-12-10 14:39:21
...                     ...
249735  2017-09-20 19:42:05
249736  2017-09-20 19:42:29
[249737 rows x 1 columns]
'''#获取分类
# print(type(df["title"])) # <class 'pandas.core.series.Series'>
tempt_list = df["title"].str.split(':').tolist()
# print(tempt_list)
'''
RESCUE - ELEVATOR'], 
[['Traffic', ' DISABLED VEHICLE -'], 
['Fire', ' FIRE ALARM'], 
['Traffic', ' ROAD OBSTRUCTION -'], 
···
['Fire', ' FIRE INVESTIGATION'],['EMS', ' UNKNOWN MEDICAL EMERGENCY'], ['Traffic', ' ROAD OBSTRUCTION -']]
'''# cate_list = set([i[0] for i in tempt_list]) # set()保证元素不重复
# print(cate_list)
'''{'Traffic', 'EMS', 'Fire'} '''cate_list = list(set([i[0] for i in tempt_list]))
# print(cate_list)
'''['Traffic', 'EMS', 'Fire']'''#构造全为0的数组
zero_array = pd.DataFrame(np.zeros((df.shape[0], len(cate_list))), columns=cate_list)
# print(zero_array)
'''EMS  Fire  Traffic
0       0.0   0.0      0.0
1       0.0   0.0      0.0
2       0.0   0.0      0.0
...     ...   ...      ...
249735  0.0   0.0      0.0
249736  0.0   0.0      0.0
[249737 rows x 3 columns]
'''#赋值
# print(df["title"].str.contains('EMS'))
'''
0          True
1          True
2         False...  
249734     True
249735    False
249736    False
Name: title, Length: 249737, dtype: bool
'''for cate in cate_list:# 高效赋值zero_array[cate][df["title"].str.contains(cate)] = 1# print(zero_array)
'''Fire  Traffic  EMS
0        0.0      0.0  1.0
1        0.0      0.0  1.0
...      ...      ...  ...
249735   1.0      0.0  0.0
249736   0.0      1.0  0.0[249737 rows x 3 columns]
'''sum_data = zero_array.sum(axis=0)
# print(sum_data)
'''
EMS        124844.0
Traffic     87465.0
Fire        37432.0
dtype: float64
'''plt.figure(figsize=(20, 8), dpi=80)_x = sum_data.keys()
_y = sum_data.valuesplt.bar(range(len(_x)), _y, width=0.1)# 给柱状图标注数值,注意zip(x, y),x的位置不能放_x??
for x, y in zip(range(len(_x)), _y):plt.text(x, y+0.5, y.astype(int), ha='center', va='bottom')
plt.xticks(range(len(_x)), _x)plt.show()

2.pandas的时间序列_代码示例

import pandas as pd
import numpy as npdate_list = pd.date_range(start="2021-1-1", end="2022-1-1", freq='M')
# print(date_list)
'''
DatetimeIndex(['2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30','2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31','2021-09-30', '2021-10-31', '2021-11-30', '2021-12-31'],dtype='datetime64[ns]', freq='M')
'''# print(pd.date_range(start="20210101", periods=4, freq="H"))
'''
DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 01:00:00','2021-01-01 02:00:00', '2021-01-01 03:00:00'],dtype='datetime64[ns]', freq='H')
'''index = pd.date_range(start="20220101", periods=5)
df = pd.DataFrame(np.random.rand(5), index=index)
# print(df)
'''0
2022-01-01  0.269013
2022-01-02  0.748808
2022-01-03  0.711873
2022-01-04  0.714188
2022-01-05  0.513025
'''

3.911的时间统计_代码示例

from matplotlib import pyplot as plt
import pandas as pd
import numpy as npdf = pd.read_csv("./911.csv")
# print(df.info())
'''
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 249737 entries, 0 to 249736
Data columns (total 9 columns):#   Column     Non-Null Count   Dtype  
---  ------     --------------   -----  0   lat        249737 non-null  float641   lng        249737 non-null  float642   desc       249737 non-null  object 3   zip        219391 non-null  float644   title      249737 non-null  object 5   timeStamp  249737 non-null  object 6   twp        249644 non-null  object 7   addr       249737 non-null  object 8   e          249737 non-null  int64  
dtypes: float64(3), int64(1), object(5)
memory usage: 17.1+ MB
'''# print(df['timeStamp'])
'''
0         2015-12-10 17:10:52
1         2015-12-10 17:29:21
2         2015-12-10 14:39:21...         
249735    2017-09-20 19:42:05
249736    2017-09-20 19:42:29
Name: timeStamp, Length: 249737, dtype: object
'''df["timeStamp"] = pd.to_datetime(df["timeStamp"])
# print(df['timeStamp']) # 从object转化为了datetime64
'''
0        2015-12-10 17:10:52
1        2015-12-10 17:29:21...        
249736   2017-09-20 19:42:29
Name: timeStamp, Length: 249737, dtype: datetime64[ns]
'''df.set_index("timeStamp", inplace=True) # “timeStamp”设置为索引了
# print(df)
'''lat        lng  ...                        addr  e
timeStamp                                  ...                               
2015-12-10 17:10:52  40.297876 -75.581294  ...      REINDEER CT & DEAD END  1
2015-12-10 17:29:21  40.258061 -75.264680  ...  BRIAR PATH & WHITEMARSH LN  1
...                        ...        ...  ...                         ... ..
2017-09-20 19:42:05  40.196606 -75.423406  ...         MILL RD & CASSEL RD  1
2017-09-20 19:42:29  40.095206 -75.410735  ...          1ST AVE & MOORE RD  1[249737 rows x 8 columns]
'''#统计出911数据中不同月份电话次数的  ['title']表示取列
count_by_moth = df.resample("M").count()['title']
# print(count_by_moth)
'''
timeStamp
2015-12-31     7916
2016-01-31    13096
···
2017-08-31    11753
2017-09-30     7276
Freq: M, Name: title, dtype: int64
'''plt.figure(figsize=(20, 8), dpi=60)
_x = count_by_moth.index
_y = count_by_moth.values_x = [i.strftime('%Y%m%d') for i in _x] # 只取年月日plt.plot(range(len(_x)), _y)
plt.xticks(range(len(_x)), _x, rotation=45)plt.show()

4.911的复合统计_代码示例

from matplotlib import pyplot as plt
import pandas as pd
import numpy as np#911数据中不同月份不同类型的电话的次数的变化情况
df = pd.read_csv("./911.csv")#把时间字符串转为时间类型
df["timeStamp"] = pd.to_datetime(df['timeStamp'])
# print(df["timeStamp"])
'''
0        2015-12-10 17:10:52
1        2015-12-10 17:29:21...        
249736   2017-09-20 19:42:29
Name: timeStamp, Length: 249737, dtype: datetime64[ns]
'''#表示分类
temp_list = df["title"].str.split(":").tolist()
cate_list = [i[0] for i in temp_list]
# print(cate_list)
'''
['EMS', 'EMS', ··· 'Fire', 'Traffic']
'''
#添加列
df['cate'] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1)))# 设置时间为索引 (原地改变)
df.set_index("timeStamp", inplace=True)# print(df['cate'])
'''
timeStamp
2015-12-10 17:10:52        EMS
2015-12-10 17:29:21        EMS···               ...   
2017-09-20 19:42:29    Traffic
Name: cate, Length: 249737, dtype: object
'''# print(pd.DataFrame(df.groupby(by="cate"))) #??????
'''0                        1
0      EMS          lat        lng  ......
1     Fire          lat        lng  ......
2  Traffic          lat        lng  ......
'''plt.figure(figsize=(20, 8), dpi=60)
#分组 ????
for group_name, group_data in df.groupby(by="cate"):# 对不同的分类都进行绘图count_by_moth = group_data.resample("M").count()['title']#画图_x = count_by_moth.index_y = count_by_moth.values_x = [i.strftime('%Y%m%d') for i in _x]plt.plot(range(len(_x)), _y, label=group_name)plt.xticks(range(len(_x)), _x, rotation=45)
plt.legend(loc="best")
plt.show()

5.pm2.5的统计_代码示例

from matplotlib import pyplot as plt
import pandas as pd
import numpy as npdf = pd.read_csv("./PM2.5/BeijingPM20100101_20151231.csv")# print(df.head(1))
'''No  year  month  day  hour  ...  TEMP  cbwd   Iws  precipitation  Iprec
0   1  2010      1    1     0  ... -11.0    NW  1.79            0.0    0.0
'''# print(df.info())
'''
[1 rows x 18 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 52584 entries, 0 to 52583
Data columns (total 18 columns):#   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  0   No               52584 non-null  int64  1   year             52584 non-null  int64  2   month            52584 non-null  int64  3   day              52584 non-null  int64  4   hour             52584 non-null  int64  5   season           52584 non-null  int64  6   PM_Dongsi        25052 non-null  float647   PM_Dongsihuan    20508 non-null  float648   PM_Nongzhanguan  24931 non-null  float649   PM_US Post       50387 non-null  float6410  DEWP             52579 non-null  float6411  HUMI             52245 non-null  float6412  PRES             52245 non-null  float6413  TEMP             52579 non-null  float6414  cbwd             52579 non-null  object 15  Iws              52579 non-null  float6416  precipitation    52100 non-null  float6417  Iprec            52100 non-null  float64
dtypes: float64(11), int64(6), object(1)
memory usage: 7.2+ MB
'''#把分开的时间字符串通过periodIndex的方法转化为pandas的时间类型
period = pd.PeriodIndex(year=df['year'], month=df['month'],day=df['day'], hour=df['hour'], freq="H")
# print(period)
'''
PeriodIndex(['2010-01-01 00:00', '2010-01-01 01:00', '2010-01-01 02:00',...015-12-31 21:00', '2015-12-31 22:00','2015-12-31 23:00'],dtype='period[H]', length=52584, freq='H')
'''# 设置period列
df['date_time'] =period#把datetime 设置为索引
df.set_index("date_time", inplace=True)#进行降采样
df = df.resample("7D").mean()
# print(df.head())
'''No    year     month  ...        Iws  precipitation     Iprec
date_time                            ...                                    
2010-01-01   84.5  2010.0  1.000000  ...  43.859821       0.066667  0.786905
2010-01-08  252.5  2010.0  1.000000  ...  45.392083       0.000000  0.000000
2010-01-15  420.5  2010.0  1.000000  ...  17.492976       0.000000  0.000000
2010-01-22  588.5  2010.0  1.000000  ...  54.854048       0.000000  0.000000
2010-01-29  756.5  2010.0  1.571429  ...  26.625119       0.000000  0.000000
[5 rows x 17 columns]
'''#处理缺失数据，删除缺失数据
# print(df["PM_US Post"])# data = df["PM_US Post"].drapna() # dropna()删除NaN数据data = df["PM_US Post"]
data_china = df["PM_Dongsihuan"]plt.figure(figsize=(20, 8), dpi=60)
_x = data.index
_y = data.values# _x = [i.strftime("%Y%m%d") for i in _x] # 截取表示的时间需要的话可以用
# _x_china = [i.strftime("%Y%m%d") for i in data_china.index]_y_china = data_china.valuesplt.plot(range(len(_x)), _y, label="us")
plt.plot(range(len(_x)), _y_china, label="cn")# range 和 list 都可以设置步长
plt.xticks(range(0, len(_x), 10), list(_x)[::10], rotation=45)
plt.legend(loc="best")plt.show()