21.Pandas怎样快捷方便的处理日期数据
21.Pandas怎样快捷方便的处理日期数据
Pandas日期处理的作用：将2018-01-01、1/1/2018等多种日期格式映射成统一的格式对象，在该对象上提供强大的功能支持
几个概念：
- pd.to_datetime：pandas的一个函数，能将字符串、列表、series变成日期形式
- Timestamp：pandas表示日期的对象形式
- DatetimeIndex：pandas表示日期的对象列表形式
其中：
- DatetimeIndex是Timestamp的列表形式
- pd.to_datetime对单个日期字符串处理会得到Timestamp
- pd.to_datetime对日期字符串列表处理会得到DatetimeIndex
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问题：怎样统计每周、每月、每季度的最高温度？
1、读取天气数据到dataframe
In [1]:
import pandas as pd
%matplotlib inline
In [2]:
fpath = "./datas/beijing_tianqi/beijing_tianqi_2018.csv"
df = pd.read_csv(fpath)
# 替换掉温度的后缀℃
df.loc[:, "bWendu"] = df["bWendu"].str.replace("℃", "").astype('int32')
df.loc[:, "yWendu"] = df["yWendu"].str.replace("℃", "").astype('int32')
df.head()
Out[2]:
| ymd | bWendu | yWendu | tianqi | fengxiang | fengli | aqi | aqiInfo | aqiLevel | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 3 | -6 | 晴~多云 | 东北风 | 1-2级 | 59 | 良 | 2 |
| 1 | 2018-01-02 | 2 | -5 | 阴~多云 | 东北风 | 1-2级 | 49 | 优 | 1 |
| 2 | 2018-01-03 | 2 | -5 | 多云 | 北风 | 1-2级 | 28 | 优 | 1 |
| 3 | 2018-01-04 | 0 | -8 | 阴 | 东北风 | 1-2级 | 28 | 优 | 1 |
| 4 | 2018-01-05 | 3 | -6 | 多云~晴 | 西北风 | 1-2级 | 50 | 优 | 1 |
2、将日期列转换成pandas的日期
In [3]:
df.set_index(pd.to_datetime(df["ymd"]), inplace=True)
In [4]:
df.head()
Out[4]:
| ymd | bWendu | yWendu | tianqi | fengxiang | fengli | aqi | aqiInfo | aqiLevel | |
|---|---|---|---|---|---|---|---|---|---|
| ymd | |||||||||
| 2018-01-01 | 2018-01-01 | 3 | -6 | 晴~多云 | 东北风 | 1-2级 | 59 | 良 | 2 |
| 2018-01-02 | 2018-01-02 | 2 | -5 | 阴~多云 | 东北风 | 1-2级 | 49 | 优 | 1 |
| 2018-01-03 | 2018-01-03 | 2 | -5 | 多云 | 北风 | 1-2级 | 28 | 优 | 1 |
| 2018-01-04 | 2018-01-04 | 0 | -8 | 阴 | 东北风 | 1-2级 | 28 | 优 | 1 |
| 2018-01-05 | 2018-01-05 | 3 | -6 | 多云~晴 | 西北风 | 1-2级 | 50 | 优 | 1 |
In [5]:
df.index
Out[5]:
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
'2018-01-09', '2018-01-10',
...
'2018-12-22', '2018-12-23', '2018-12-24', '2018-12-25',
'2018-12-26', '2018-12-27', '2018-12-28', '2018-12-29',
'2018-12-30', '2018-12-31'],
dtype='datetime64[ns]', name='ymd', length=365, freq=None)
In [6]:
# DatetimeIndex是Timestamp的列表形式
df.index[0]
Out[6]:
Timestamp('2018-01-01 00:00:00')
3、 方便的对DatetimeIndex进行查询
In [7]:
# 筛选固定的某一天
df.loc['2018-01-05']
Out[7]:
ymd 2018-01-05
bWendu 3
yWendu -6
tianqi 多云~晴
fengxiang 西北风
fengli 1-2级
aqi 50
aqiInfo 优
aqiLevel 1
Name: 2018-01-05 00:00:00, dtype: object
In [8]:
# 日期区间
df.loc['2018-01-05':'2018-01-10']
Out[8]:
| ymd | bWendu | yWendu | tianqi | fengxiang | fengli | aqi | aqiInfo | aqiLevel | |
|---|---|---|---|---|---|---|---|---|---|
| ymd | |||||||||
| 2018-01-05 | 2018-01-05 | 3 | -6 | 多云~晴 | 西北风 | 1-2级 | 50 | 优 | 1 |
| 2018-01-06 | 2018-01-06 | 2 | -5 | 多云~阴 | 西南风 | 1-2级 | 32 | 优 | 1 |
| 2018-01-07 | 2018-01-07 | 2 | -4 | 阴~多云 | 西南风 | 1-2级 | 59 | 良 | 2 |
| 2018-01-08 | 2018-01-08 | 2 | -6 | 晴 | 西北风 | 4-5级 | 50 | 优 | 1 |
| 2018-01-09 | 2018-01-09 | 1 | -8 | 晴 | 西北风 | 3-4级 | 34 | 优 | 1 |
| 2018-01-10 | 2018-01-10 | -2 | -10 | 晴 | 西北风 | 1-2级 | 26 | 优 | 1 |
In [10]:
# 按月份前缀筛选
df.loc['2018-03']
Out[10]:
| ymd | bWendu | yWendu | tianqi | fengxiang | fengli | aqi | aqiInfo | aqiLevel | |
|---|---|---|---|---|---|---|---|---|---|
| ymd | |||||||||
| 2018-03-01 | 2018-03-01 | 8 | -3 | 多云 | 西南风 | 1-2级 | 46 | 优 | 1 |
| 2018-03-02 | 2018-03-02 | 9 | -1 | 晴~多云 | 北风 | 1-2级 | 95 | 良 | 2 |
| 2018-03-03 | 2018-03-03 | 13 | 3 | 多云~阴 | 北风 | 1-2级 | 214 | 重度污染 | 5 |
| 2018-03-04 | 2018-03-04 | 7 | -2 | 阴~多云 | 东南风 | 1-2级 | 144 | 轻度污染 | 3 |
| 2018-03-05 | 2018-03-05 | 8 | -3 | 晴 | 南风 | 1-2级 | 94 | 良 | 2 |
| 2018-03-06 | 2018-03-06 | 6 | -3 | 多云~阴 | 东南风 | 3-4级 | 67 | 良 | 2 |
| 2018-03-07 | 2018-03-07 | 6 | -2 | 阴~多云 | 北风 | 1-2级 | 65 | 良 | 2 |
| 2018-03-08 | 2018-03-08 | 8 | -4 | 晴 | 东北风 | 1-2级 | 62 | 良 | 2 |
| 2018-03-09 | 2018-03-09 | 10 | -2 | 多云 | 西南风 | 1-2级 | 132 | 轻度污染 | 3 |
| 2018-03-10 | 2018-03-10 | 14 | -2 | 晴 | 东南风 | 1-2级 | 171 | 中度污染 | 4 |
| 2018-03-11 | 2018-03-11 | 11 | 0 | 多云 | 南风 | 1-2级 | 81 | 良 | 2 |
| 2018-03-12 | 2018-03-12 | 15 | 3 | 多云~晴 | 南风 | 1-2级 | 174 | 中度污染 | 4 |
| 2018-03-13 | 2018-03-13 | 17 | 5 | 晴~多云 | 南风 | 1-2级 | 287 | 重度污染 | 5 |
| 2018-03-14 | 2018-03-14 | 15 | 6 | 多云~阴 | 东北风 | 1-2级 | 293 | 重度污染 | 5 |
| 2018-03-15 | 2018-03-15 | 12 | -1 | 多云~晴 | 东北风 | 3-4级 | 70 | 良 | 2 |
| 2018-03-16 | 2018-03-16 | 10 | -1 | 多云 | 南风 | 1-2级 | 58 | 良 | 2 |
| 2018-03-17 | 2018-03-17 | 4 | 0 | 小雨~阴 | 南风 | 1-2级 | 81 | 良 | 2 |
| 2018-03-18 | 2018-03-18 | 13 | 1 | 多云~晴 | 西南风 | 1-2级 | 134 | 轻度污染 | 3 |
| 2018-03-19 | 2018-03-19 | 13 | 2 | 多云 | 东风 | 1-2级 | 107 | 轻度污染 | 3 |
| 2018-03-20 | 2018-03-20 | 10 | -2 | 多云 | 南风 | 1-2级 | 41 | 优 | 1 |
| 2018-03-21 | 2018-03-21 | 11 | 1 | 多云 | 西南风 | 1-2级 | 76 | 良 | 2 |
| 2018-03-22 | 2018-03-22 | 17 | 4 | 晴~多云 | 西南风 | 1-2级 | 112 | 轻度污染 | 3 |
| 2018-03-23 | 2018-03-23 | 18 | 5 | 多云 | 北风 | 1-2级 | 146 | 轻度污染 | 3 |
| 2018-03-24 | 2018-03-24 | 22 | 5 | 晴 | 西南风 | 1-2级 | 119 | 轻度污染 | 3 |
| 2018-03-25 | 2018-03-25 | 24 | 7 | 晴 | 南风 | 1-2级 | 78 | 良 | 2 |
| 2018-03-26 | 2018-03-26 | 25 | 7 | 多云 | 西南风 | 1-2级 | 151 | 中度污染 | 4 |
| 2018-03-27 | 2018-03-27 | 27 | 11 | 晴 | 南风 | 1-2级 | 243 | 重度污染 | 5 |
| 2018-03-28 | 2018-03-28 | 25 | 9 | 多云~晴 | 东风 | 1-2级 | 387 | 严重污染 | 6 |
| 2018-03-29 | 2018-03-29 | 19 | 7 | 晴 | 南风 | 1-2级 | 119 | 轻度污染 | 3 |
| 2018-03-30 | 2018-03-30 | 18 | 8 | 多云 | 南风 | 1-2级 | 68 | 良 | 2 |
| 2018-03-31 | 2018-03-31 | 23 | 9 | 多云~晴 | 南风 | 1-2级 | 125 | 轻度污染 | 3 |
In [11]:
# 按月份前缀筛选
df.loc["2018-07":"2018-09"].index
Out[11]:
DatetimeIndex(['2018-07-01', '2018-07-02', '2018-07-03', '2018-07-04',
'2018-07-05', '2018-07-06', '2018-07-07', '2018-07-08',
'2018-07-09', '2018-07-10', '2018-07-11', '2018-07-12',
'2018-07-13', '2018-07-14', '2018-07-15', '2018-07-16',
'2018-07-17', '2018-07-18', '2018-07-19', '2018-07-20',
'2018-07-21', '2018-07-22', '2018-07-23', '2018-07-24',
'2018-07-25', '2018-07-26', '2018-07-27', '2018-07-28',
'2018-07-29', '2018-07-30', '2018-07-31', '2018-08-01',
'2018-08-02', '2018-08-03', '2018-08-04', '2018-08-05',
'2018-08-06', '2018-08-07', '2018-08-08', '2018-08-09',
'2018-08-10', '2018-08-11', '2018-08-12', '2018-08-13',
'2018-08-14', '2018-08-15', '2018-08-16', '2018-08-17',
'2018-08-18', '2018-08-19', '2018-08-20', '2018-08-21',
'2018-08-22', '2018-08-23', '2018-08-24', '2018-08-25',
'2018-08-26', '2018-08-27', '2018-08-28', '2018-08-29',
'2018-08-30', '2018-08-31', '2018-09-01', '2018-09-02',
'2018-09-03', '2018-09-04', '2018-09-05', '2018-09-06',
'2018-09-07', '2018-09-08', '2018-09-09', '2018-09-10',
'2018-09-11', '2018-09-12', '2018-09-13', '2018-09-14',
'2018-09-15', '2018-09-16', '2018-09-17', '2018-09-18',
'2018-09-19', '2018-09-20', '2018-09-21', '2018-09-22',
'2018-09-23', '2018-09-24', '2018-09-25', '2018-09-26',
'2018-09-27', '2018-09-28', '2018-09-29', '2018-09-30'],
dtype='datetime64[ns]', name='ymd', freq=None)
In [12]:
# 按年份前缀筛选
df.loc["2018"].head()
Out[12]:
| ymd | bWendu | yWendu | tianqi | fengxiang | fengli | aqi | aqiInfo | aqiLevel | |
|---|---|---|---|---|---|---|---|---|---|
| ymd | |||||||||
| 2018-01-01 | 2018-01-01 | 3 | -6 | 晴~多云 | 东北风 | 1-2级 | 59 | 良 | 2 |
| 2018-01-02 | 2018-01-02 | 2 | -5 | 阴~多云 | 东北风 | 1-2级 | 49 | 优 | 1 |
| 2018-01-03 | 2018-01-03 | 2 | -5 | 多云 | 北风 | 1-2级 | 28 | 优 | 1 |
| 2018-01-04 | 2018-01-04 | 0 | -8 | 阴 | 东北风 | 1-2级 | 28 | 优 | 1 |
| 2018-01-05 | 2018-01-05 | 3 | -6 | 多云~晴 | 西北风 | 1-2级 | 50 | 优 | 1 |
4、方便的获取周、月、季度
Timestamp、DatetimeIndex支持大量的属性可以获取日期分量：
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-date-components
In [13]:
# 周数字列表
df.index.week
Out[13]:
Int64Index([ 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,
...
51, 51, 52, 52, 52, 52, 52, 52, 52, 1],
dtype='int64', name='ymd', length=365)
In [14]:
# 月数字列表
df.index.month
Out[14]:
Int64Index([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
...
12, 12, 12, 12, 12, 12, 12, 12, 12, 12],
dtype='int64', name='ymd', length=365)
In [15]:
# 季度数字列表
df.index.quarter
Out[15]:
Int64Index([1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
...
4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
dtype='int64', name='ymd', length=365)
5、统计每周、每月、每个季度的最高温度
统计每周的数据
In [16]:
df.groupby(df.index.week)["bWendu"].max().head()
Out[16]:
ymd
1 3
2 6
3 7
4 -1
5 4
Name: bWendu, dtype: int32
In [17]:
df.groupby(df.index.week)["bWendu"].max().plot()
Out[17]:
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)]
统计每个月的数据
In [18]:
df.groupby(df.index.month)["bWendu"].max()
Out[18]:
ymd
1 7
2 12
3 27
4 30
5 35
6 38
7 37
8 36
9 31
10 25
11 18
12 10
Name: bWendu, dtype: int32
In [19]:
df.groupby(df.index.month)["bWendu"].max().plot()
Out[19]:
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)]
统计每个季度的数据
In [20]:
df.groupby(df.index.quarter)["bWendu"].max()
Out[20]:
ymd
1 27
2 38
3 37
4 25
Name: bWendu, dtype: int32
In [21]:
df.groupby(df.index.quarter)["bWendu"].max().plot()
Out[21]:
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)]
import pandas as pd
import matplotlib.pyplot as plot
df=pd.read_csv('./beijing_tianqi_2018.csv')
print(df.head())
#替换后缀
df['bWendu']=df['bWendu'].str.replace("℃", "").astype('int32')
df['yWendu']=df['yWendu'].str.replace("℃", "").astype('int32')
print(df.head())
print(df.index)
print('----------------------------------------')
df.set_index(pd.to_datetime(df['ymd']),inplace=True)
print(df.head())
print(df.index)
print(df.index[0])
print(df.loc['2018-01-05'])
print(df.loc['2018-01-05':'2018-01-10'])
print(df.loc['2018-03'])
print(df.loc['2018-07':'2018-09'].index)
print(df.loc['2018'].head())
'''
Timestamp、DatetimeIndex支持大量的属性可以获取日期分量：
使用 Pandas 库的 Index 类型，
通过调用 isocalendar 方法获取每个日期所在的 ISO 格式下的年份、周数和星期几，
并返回它们的周数作为索引。
通过 dtype='int64' 参数显式给出了数据类型，
以确保与以前使用的 Int64Index 索引具有相同的行为。
'''
print(pd.Index(df.index.isocalendar().week,dtype='int64'))
mm=df.index.month
print(mm)
quar=df.index.quarter
print(quar)
ymd=df.groupby(df.index.isocalendar().week)['bWendu'].max().head()
print(ymd)
# df.groupby(df.index.isocalendar().week)['bWendu'].max().plot()
# # plot.show()
ysj=df.groupby(df.index.month)['bWendu'].max()
print(ysj)
# df.groupby(df.index.month)['bWendu'].max().plot()
# plot.show()
jdj=df.groupby(df.index.quarter)['bWendu'].max()
print(jdj)
df.groupby(df.index.quarter)['bWendu'].max().plot()
plot.show()