#时间序列
import pandas as pd
import numpy as np
# 生成一段时间范围
''' 该函数主要用于生成一个固定频率的时间索引，在调用构造方法时，必须指定start、end、periods中的两个参数值，否则 报错。
时间序列频率:
D 日历日的每天 B 工作日的每天 H 每小时 T或min 每分钟
S 每秒
L或ms U
M
BM
MS BMS
每毫秒
每微秒
日历日的月底日期
工作日的月底日期
日历日的月初日期
工作日的月初日期
'''
date = pd.date_range(start='20190501',end='20190530')
print(date)
print("-"*20)
#freq:日期偏移量，取值为string或DateOffset，默认为'D'， freq='1h30min' freq='10D' # periods:固定时期，取值为整数或None
date = pd.date_range(start='20190501',periods=10,freq='10D')
print(date)
print("-"*20)
#时间序列在dataFrame中的作用
#可以将时间作为索引
index = pd.date_range(start='20190101',periods=10)
df = pd.Series(np.random.randint(0,10,size = 10),index=index)
print(df)
print("-"*20)
long_ts = pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2019',periods=1000))
print(long_ts)
print("-"*20)
#根据年份获取
result = long_ts['2020']
print(result)
print("-"*20)
#年份和日期获取
result = long_ts['2020-05']
print(result)
print("-"*20)
#使用切片
result = long_ts['2020-05-01':'2020-05-06']
print(result)
print("-"*20)
#通过between_time()返回位于指定时间段的数据集
index=pd.date_range("2018-03-17","2018-03-30",freq="2H")
ts = pd.Series(np.random.randn(157),index=index)
print(ts.between_time("7:00","17:00"))
print("-"*20)
#这些操作也都适用于dataframe
index=pd.date_range('1/1/2019',periods=100)
df = pd.DataFrame(np.random.randn(100,4),index=index)
print(df.loc['2019-04'])
输出：
/Users/lazy/PycharmProjects/matplotlib/venv/bin/python /Users/lazy/PycharmProjects/matplotlib/drawing.py
DatetimeIndex(['2019-05-01', '2019-05-02', '2019-05-03', '2019-05-04',
'2019-05-05', '2019-05-06', '2019-05-07', '2019-05-08',
'2019-05-09', '2019-05-10', '2019-05-11', '2019-05-12',
'2019-05-13', '2019-05-14', '2019-05-15', '2019-05-16',
'2019-05-17', '2019-05-18', '2019-05-19', '2019-05-20',
'2019-05-21', '2019-05-22', '2019-05-23', '2019-05-24',
'2019-05-25', '2019-05-26', '2019-05-27', '2019-05-28',
'2019-05-29', '2019-05-30'],
dtype='datetime64[ns]', freq='D')
--------------------
DatetimeIndex(['2019-05-01', '2019-05-11', '2019-05-21', '2019-05-31',
'2019-06-10', '2019-06-20', '2019-06-30', '2019-07-10',
'2019-07-20', '2019-07-30'],
dtype='datetime64[ns]', freq='10D')
--------------------
2019-01-01 9
2019-01-02 8
2019-01-03 9
2019-01-04 2
2019-01-05 4
2019-01-06 4
2019-01-07 0
2019-01-08 1
2019-01-09 4
2019-01-10 1
Freq: D, dtype: int64
--------------------
2019-01-01 1.161118
2019-01-02 0.342857
2019-01-03 1.581292
2019-01-04 -0.928493
2019-01-05 -1.406328
...
2021-09-22 0.106048
2021-09-23 0.228015
2021-09-24 -0.201558
2021-09-25 1.136008
2021-09-26 -0.947871
Freq: D, Length: 1000, dtype: float64
--------------------
2020-01-01 1.828810
2020-01-02 1.425193
2020-01-03 -0.258607
2020-01-04 -0.390869
2020-01-05 -0.509062
...
2020-12-27 0.155428
2020-12-28 -0.450071
2020-12-29 -0.050287
2020-12-30 0.033996
2020-12-31 -0.783760
Freq: D, Length: 366, dtype: float64
--------------------
2020-05-01 0.843815
2020-05-02 -0.189866
2020-05-03 0.206807
2020-05-04 -0.279099
2020-05-05 0.575256
2020-05-06 -0.163009
2020-05-07 -0.850285
2020-05-08 -0.602792
2020-05-09 -0.630393
2020-05-10 -1.447383
2020-05-11 0.664726
2020-05-12 -0.108902
2020-05-13 0.333349
2020-05-14 1.068075
2020-05-15 -0.004767
2020-05-16 0.178172
2020-05-17 1.189467
2020-05-18 2.149068
2020-05-19 0.501122
2020-05-20 0.025200
2020-05-21 0.459819
2020-05-22 -0.688207
2020-05-23 -0.560723
2020-05-24 -0.448853
2020-05-25 0.612620
2020-05-26 0.781641
2020-05-27 0.225619
2020-05-28 -0.026749
2020-05-29 -0.020273
2020-05-30 0.812233
2020-05-31 -1.258738
Freq: D, dtype: float64
--------------------
2020-05-01 0.843815
2020-05-02 -0.189866
2020-05-03 0.206807
2020-05-04 -0.279099
2020-05-05 0.575256
2020-05-06 -0.163009
Freq: D, dtype: float64
--------------------
2018-03-17 08:00:00 0.704187
2018-03-17 10:00:00 0.496051
2018-03-17 12:00:00 1.828923
2018-03-17 14:00:00 -0.096337
2018-03-17 16:00:00 1.584530
...
2018-03-29 08:00:00 0.779002
2018-03-29 10:00:00 -0.244056
2018-03-29 12:00:00 -0.428603
2018-03-29 14:00:00 1.297126
2018-03-29 16:00:00 0.482789
Length: 65, dtype: float64
--------------------
0 1 2 3
2019-04-01 -2.074822 -0.939817 0.321402 -0.627823
2019-04-02 1.368356 0.150809 1.102027 -0.286527
2019-04-03 0.422506 -0.024193 -0.857528 1.061103
2019-04-04 -0.324066 -0.764358 -0.586841 1.520979
2019-04-05 1.398816 1.088023 -0.940833 1.249962
2019-04-06 -0.031951 0.905921 0.455782 -0.968012
2019-04-07 1.421253 -0.786199 0.875216 0.551437
2019-04-08 1.015066 -1.051041 0.430193 -0.014169
2019-04-09 0.279851 0.824598 -0.606735 -1.411600
2019-04-10 -0.252020 -0.408230 -0.698608 0.158843
import pandas as pd
import numpy as np
ts = pd.Series(np.random.randn(10),index=pd.date_range('1/1/2019',periods=10))
print(ts)
print("-"*20)
# 移动数据，索引不变，默认由NaN填充
# periods: 移动的位数 负数是向上移动
# fill_value: 移动后填充数据
print(ts.shift(periods=2,fill_value=100))
print("-"*20)
# 通过tshift()将索引移动指定的时间:
print(ts.tshift(2))
print("-"*20)
# 将时间戳转化成时间根式
print(pd.to_datetime(1554970740000,unit='ms'))
print("-"*20)
# utc是协调世界时,时区是以UTC的偏移量的形式表示的,但是注意设置utc=True,是让pandas对象具有时区性质,对于一列 进行转换的,会造成转换错误
# unit='ms' 设置粒度是到毫秒级别的
print(pd.to_datetime(1554970740000,unit='ms').tz_localize('UTC').tz_convert('Asia/Shanghai'))
print("-"*20)
# 处理一列
df = pd.DataFrame([1554970740000, 1554970800000, 1554970860000],columns = ['time_stamp'])
print(pd.to_datetime(df['time_stamp'],unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')) #先赋予标准时区,再转换到东八区
print("-"*20)
# 处理中文
print(pd.to_datetime('2019年10月10日',format='%Y年%m月%d日'))
输出：
/Users/lazy/PycharmProjects/matplotlib/venv/bin/python /Users/lazy/PycharmProjects/matplotlib/drawing.py
2019-01-01 -2.679356
2019-01-02 0.775274
2019-01-03 -0.045711
2019-01-04 0.883532
2019-01-05 -0.941213
2019-01-06 -1.461701
2019-01-07 0.149344
2019-01-08 -0.185037
2019-01-09 -0.754532
2019-01-10 0.561909
Freq: D, dtype: float64
--------------------
2019-01-01 100.000000
2019-01-02 100.000000
2019-01-03 -2.679356
2019-01-04 0.775274
2019-01-05 -0.045711
2019-01-06 0.883532
2019-01-07 -0.941213
2019-01-08 -1.461701
2019-01-09 0.149344
2019-01-10 -0.185037
Freq: D, dtype: float64
--------------------
2019-01-03 -2.679356
2019-01-04 0.775274
2019-01-05 -0.045711
2019-01-06 0.883532
2019-01-07 -0.941213
2019-01-08 -1.461701
2019-01-09 0.149344
2019-01-10 -0.185037
2019-01-11 -0.754532
2019-01-12 0.561909
Freq: D, dtype: float64
--------------------
2019-04-11 08:19:00
--------------------
2019-04-11 16:19:00+08:00
--------------------
0 2019-04-11 16:19:00+08:00
1 2019-04-11 16:20:00+08:00
2 2019-04-11 16:21:00+08:00
Name: time_stamp, dtype: datetime64[ns, Asia/Shanghai]
--------------------
2019-10-10 00:00:00
# 分组 import pandas as pd import numpy as np df=pd.DataFrame({ 'name':['BOSS','Lilei','Lilei','Han','BOSS','BOSS','Han','BOSS'], 'Year':[2016,2016,2016,2016,2017,2017,2017,2017], 'Salary':[999999,20000,25000,3000,9999999,999999,3500,999999], 'Bonus':[100000,20000,20000,5000,200000,300000,3000,400000] }) print(df) print("-"*20) # 根据name这一列进行分组 group_by_name=df.groupby('name') print(type(group_by_name)) print("-"*20) # 查看分组 print(group_by_name.groups) # 分组后的数量 print("-"*20) print(group_by_name.count()) print("-"*20) # 查看分组的情况 for name,group in group_by_name: print(name) # 组的名字 print(group) # 组具体内容 print("-"*20) # 按照某一列进行分组, 将name这一列作为分组的键，对year进行分组 group_by_name=df['Year'].groupby(df['name']) print(group_by_name.count()) print("-"*20) # 按照多列进行分组 group_by_name_year=df.groupby(['name','Year']) for name,group in group_by_name_year: print(name)# 组的名字 print(group)# 组具体内容 print("-" * 20) #可以选择分组 print(group_by_name.get_group('BOSS')) print("-"*20) #可以选择分组 print(group_by_name_year.get_group(('BOSS',2016))) 输出： name Year Salary Bonus 0 BOSS 2016 999999 100000 1 Lilei 2016 20000 20000 2 Lilei 2016 25000 20000 3 Han 2016 3000 5000 4 BOSS 2017 9999999 200000 5 BOSS 2017 999999 300000 6 Han 2017 3500 3000 7 BOSS 2017 999999 400000 -------------------- <class 'pandas.core.groupby.generic.DataFrameGroupBy'> -------------------- {'BOSS': Int64Index([0, 4, 5, 7], dtype='int64'), 'Han': Int64Index([3, 6], dtype='int64'), 'Lilei': Int64Index([1, 2], dtype='int64')} -------------------- Year Salary Bonus name BOSS 4 4 4 Han 2 2 2 Lilei 2 2 2 -------------------- BOSS name Year Salary Bonus 0 BOSS 2016 999999 100000 4 BOSS 2017 9999999 200000 5 BOSS 2017 999999 300000 7 BOSS 2017 999999 400000 Han name Year Salary Bonus 3 Han 2016 3000 5000 6 Han 2017 3500 3000 Lilei name Year Salary Bonus 1 Lilei 2016 20000 20000 2 Lilei 2016 25000 20000 -------------------- name BOSS 4 Han 2 Lilei 2 Name: Year, dtype: int64 -------------------- ('BOSS', 2016) name Year Salary Bonus 0 BOSS 2016 999999 100000 ('BOSS', 2017) name Year Salary Bonus 4 BOSS 2017 9999999 200000 5 BOSS 2017 999999 300000 7 BOSS 2017 999999 400000 ('Han', 2016) name Year Salary Bonus 3 Han 2016 3000 5000 ('Han', 2017) name Year Salary Bonus 6 Han 2017 3500 3000 ('Lilei', 2016) name Year Salary Bonus 1 Lilei 2016 20000 20000 2 Lilei 2016 25000 20000 -------------------- 0 2016 4 2017 5 2017 7 2017 Name: Year, dtype: int64 -------------------- name Year Salary Bonus 0 BOSS 2016 999999 100000
#聚合
import pandas as pd
import numpy as np
'''聚合函数
mean 计算分组平均值
count 分组中非NA值的数量
sum 非NA值的和
median 非NA值的算术中位数
std 标准差
var 方差
min 非NA值的最小值
max 非NA值的最大值
prod 非NA值的积
first 第一个非NA值
last 最后一个非NA值
mad 平均绝对偏差
mode 模
abs 绝对值
sem 平均值的标准误差
skew 样品偏斜度(三阶矩)
kurt 样品峰度(四阶矩)
quantile 样本分位数(百分位上的值)
cumsum 累积总和
cumprod 累积乘积
cummax 累积最大值
cummin 累积最小值
'''
df1=pd.DataFrame({'Data1':np.random.randint(0,10,5),
'Data2':np.random.randint(10,20,5),
'key1':list('aabba'),
'key2':list('xyyxy')})
print(df1)
print("-"*20)
# 按key1分组，进行聚合计算
# 注意:当分组后进行数值计算时，不是数值类的列(即麻烦列)会被清除
print(df1.groupby('key1').sum())
print("-"*20)
# 只算data1
print(df1['Data1'].groupby(df1['key1']).sum())
print("-"*20)
print(df1.groupby('key1')['Data1'].sum())
print("-"*20)
# 使用agg()函数做聚合运算
print(df1.groupby('key1').agg('sum'))
print("-"*20)
# 可以同时做多个聚合运算
print(df1.groupby('key1').agg(['sum','mean','std']))
print("-"*20)
# 可自定义函数，传入agg方法中 grouped.agg(func)
def peak_range(df):
"""
返回数值范围
"""
return df.max() - df.min()
print(df1.groupby('key1').agg(peak_range))
print("-"*20)
# 同时应用多个聚合函数
print(df1.groupby('key1').agg(['mean', 'std', 'count', peak_range])) # 默认列名为函数名
print("-"*20)
print(df1.groupby('key1').agg(['mean', 'std', 'count', ('range', peak_range)])) # 通过元组提 供新的列名
输出：
Data1 Data2 key1 key2
0 3 10 a x
1 2 16 a y
2 5 10 b y
3 9 16 b x
4 9 17 a y
--------------------
Data1 Data2
key1
a 14 43
b 14 26
--------------------
key1
a 14
b 14
Name: Data1, dtype: int64
--------------------
key1
a 14
b 14
Name: Data1, dtype: int64
--------------------
Data1 Data2
key1
a 14 43
b 14 26
--------------------
Data1 Data2
sum mean std sum mean std
key1
a 14 4.666667 3.785939 43 14.333333 3.785939
b 14 7.000000 2.828427 26 13.000000 4.242641
--------------------
Data1 Data2
key1
a 7 7
b 4 6
--------------------
Data1 Data2
mean std count peak_range mean std count peak_range
key1
a 4.666667 3.785939 3 7 14.333333 3.785939 3 7
b 7.000000 2.828427 2 4 13.000000 4.242641 2 6
--------------------
Data1 Data2
mean std count range mean std count range
key1
a 4.666667 3.785939 3 7 14.333333 3.785939 3 7
b 7.000000 2.828427 2 4 13.000000 4.242641 2 6
# 分组
import pandas as pd
import numpy as np
# 拓展apply函数
# apply函数是pandas里面所有函数中自由度最高的函数
df1=pd.DataFrame({'sex':list('FFMFMMF'),'smoker':list('YNYYNYY'),'age': [21,30,17,37,40,18,26],'weight':[120,100,132,140,94,89,123]})
print(df1)
print("-"*20)
def bin_age(age):
if age >=18:
return 1
else:
return 0
# 抽烟的年龄大于等18的
print(df1['age'].apply(bin_age))
print("-"*20)
df1['age'] = df1['age'].apply(bin_age)
print(df1)
print("-"*20)
# 取出抽烟和不抽烟的体重前二
def top(smoker,col,n=5):
return smoker.sort_values(by=col)[-n:]
print(df1.groupby('smoker').apply(top,col='weight',n=2))
输出：
sex smoker age weight
0 F Y 21 120
1 F N 30 100
2 M Y 17 132
3 F Y 37 140
4 M N 40 94
5 M Y 18 89
6 F Y 26 123
--------------------
0 1
1 1
2 0
3 1
4 1
5 1
6 1
Name: age, dtype: int64
--------------------
sex smoker age weight
0 F Y 1 120
1 F N 1 100
2 M Y 0 132
3 F Y 1 140
4 M N 1 94
5 M Y 1 89
6 F Y 1 123
--------------------
sex smoker age weight
smoker
N 4 M N 1 94
1 F N 1 100
Y 2 M Y 0 132
3 F Y 1 140
分组案例
# 分组 import pandas as pd import numpy as np import matplotlib import random from matplotlib import font_manager from matplotlib import pyplot as plt # 读取数据 data = pd.read_csv('~/Desktop/movie_metadata.csv') print('数据的形状:', data.shape) print("-"*20) print(data.head()) print("-"*20) # 2、处理缺失值 data = data.dropna(how='any') print(data.head()) print("-"*20) # 查看票房收入统计 # 导演vs票房总收入 group_director = data.groupby(by='director_name')['gross'].sum() # ascending升降序排列，True升序 result = group_director.sort_values() print(type(result)) print("-"*20) print(result) print("-"*20) movie_years = data.groupby('title_year')['movie_title'] print(movie_years.count().index.tolist()) print("-"*20) print(movie_years.count().values) x = movie_years.count().index.tolist() y = movie_years.count().values plt.figure(figsize=(10,8),dpi=80) plt.plot(x,y) plt.show() 输出： 数据的形状: (5043, 28) -------------------- color director_name ... aspect_ratio movie_facebook_likes 0 Color James Cameron ... 1.78 33000 1 Color Gore Verbinski ... 2.35 0 2 Color Sam Mendes ... 2.35 85000 3 Color Christopher Nolan ... 2.35 164000 4 NaN Doug Walker ... NaN 0 [5 rows x 28 columns] -------------------- color director_name ... aspect_ratio movie_facebook_likes 0 Color James Cameron ... 1.78 33000 1 Color Gore Verbinski ... 2.35 0 2 Color Sam Mendes ... 2.35 85000 3 Color Christopher Nolan ... 2.35 164000 5 Color Andrew Stanton ... 2.35 24000 [5 rows x 28 columns] -------------------- <class 'pandas.core.series.Series'> -------------------- director_name Ekachai Uekrongtham 1.620000e+02 Frank Whaley 7.030000e+02 Ricki Stern 1.111000e+03 Alex Craig Mann 1.332000e+03 Paul Bunnell 2.436000e+03 ... Sam Raimi 2.049549e+09 Tim Burton 2.071275e+09 Michael Bay 2.231243e+09 Peter Jackson 2.289968e+09 Steven Spielberg 4.114233e+09 Name: gross, Length: 1659, dtype: float64 -------------------- [1927.0, 1929.0, 1933.0, 1935.0, 1936.0, 1937.0, 1939.0, 1940.0, 1946.0, 1947.0, 1948.0, 1950.0, 1952.0, 1953.0, 1954.0, 1957.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, 1976.0, 1977.0, 1978.0, 1979.0, 1980.0, 1981.0, 1982.0, 1983.0, 1984.0, 1985.0, 1986.0, 1987.0, 1988.0, 1989.0, 1990.0, 1991.0, 1992.0, 1993.0, 1994.0, 1995.0, 1996.0, 1997.0, 1998.0, 1999.0, 2000.0, 2001.0, 2002.0, 2003.0, 2004.0, 2005.0, 2006.0, 2007.0, 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0, 2015.0, 2016.0] -------------------- [ 1 1 1 1 1 1 2 1 2 1 1 1 1 2 2 1 1 1 1 2 3 5 5 1 1 2 3 4 3 2 5 7 3 2 7 9 6 14 17 16 13 23 15 25 30 30 33 27 30 33 44 51 66 93 101 115 157 159 179 190 145 181 182 189 152 182 182 168 168 158 163 145 128 59]
