33. Pandas计算同比环比指标的3种方法

Pandas计算同比环比指标的3种方法

同比和环比：环比和同比用于描述统计数据的变化情况

• 环比：表示本次统计段与相连的上次统计段之间的比较。
  • 比如2010年中国第一季度GDP为G2010Q1亿元，第二季度GDP为G2010Q2亿元，则第二季度GDP环比增长（G2010Q2-G2010Q1)/G2010Q1；
• 同比：即同期相比，表示某个特定统计段今年与去年之间的比较。
  • 比如2009年中国第一季度GDP为G2009Q1亿元，则2010年第一季度的GDP同比增长为（G2010Q1-G2009Q1)/G2009Q1。

演示步骤：
0. 读取连续3年的天气数据

1. 方法1：pandas.Series.pct_change
2. 方法2：pandas.Series.shift
3. 方法3：pandas.Series.diff

pct_change、shift、diff，都实现了跨越多行的数据计算

0. 读取连续3年的天气数据

import pandas as pd
%matplotlib inline

fpath = "./datas/beijing_tianqi/beijing_tianqi_2017-2019.csv"
df = pd.read_csv(fpath, index_col="ymd", parse_dates=True)

df.head(3)

	bWendu	yWendu	tianqi	fengxiang	fengli	aqi	aqiInfo	aqiLevel
ymd
2017-01-01	5℃	-3℃	霾~晴	南风	1-2级	450	严重污染	6
2017-01-02	7℃	-6℃	晴~霾	南风	1-2级	246	重度污染	5
2017-01-03	5℃	-5℃	霾	南风	1-2级	320	严重污染	6

# 替换掉温度的后缀℃
df["bWendu"] = df["bWendu"].str.replace("℃", "").astype('int32')

df.head(3)

	bWendu	yWendu	tianqi	fengxiang	fengli	aqi	aqiInfo	aqiLevel
ymd
2017-01-01	5	-3℃	霾~晴	南风	1-2级	450	严重污染	6
2017-01-02	7	-6℃	晴~霾	南风	1-2级	246	重度污染	5
2017-01-03	5	-5℃	霾	南风	1-2级	320	严重污染	6

# 新的df，为每个月的平均最高温
df = df[["bWendu"]].resample("M").mean()

# 将索引按照日期升序排列
df.sort_index(ascending=True, inplace=True)

df.head()

	bWendu
ymd
2017-01-31	3.322581
2017-02-28	7.642857
2017-03-31	14.129032
2017-04-30	23.700000
2017-05-31	29.774194

df.index

DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',
               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',
               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31',
               '2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',
               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31',
               '2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',
               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',
               '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31'],
              dtype='datetime64[ns]', name='ymd', freq='M')

df.plot()

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方法1：pandas.Series.pct_change

pct_change方法直接算好了"(新-旧)/旧"的百分比

官方文档地址：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.pct_change.html

df["bWendu_way1_huanbi"] = df["bWendu"].pct_change(periods=1)
df["bWendu_way1_tongbi"] = df["bWendu"].pct_change(periods=12)

df.head(15)

	bWendu	bWendu_way1_huanbi	bWendu_way1_tongbi
ymd
2017-01-31	3.322581	NaN	NaN
2017-02-28	7.642857	1.300277	NaN
2017-03-31	14.129032	0.848658	NaN
2017-04-30	23.700000	0.677397	NaN
2017-05-31	29.774194	0.256295	NaN
2017-06-30	30.966667	0.040051	NaN
2017-07-31	31.612903	0.020869	NaN
2017-08-31	30.129032	-0.046939	NaN
2017-09-30	27.866667	-0.075089	NaN
2017-10-31	17.225806	-0.381849	NaN
2017-11-30	9.566667	-0.444632	NaN
2017-12-31	4.483871	-0.531303	NaN
2018-01-31	1.322581	-0.705036	-0.601942
2018-02-28	4.892857	2.699477	-0.359813
2018-03-31	14.129032	1.887685	0.000000

方法2：pandas.Series.shift

shift用于移动数据，但是保持索引不变

官方文档地址：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.shift.html

# 见识一下shift做了什么事情
# 使用pd.concat合并Series列表变成一个大的df
pd.concat(
    [df["bWendu"], 
     df["bWendu"].shift(periods=1), 
     df["bWendu"].shift(periods=12)],
    axis=1
).head(15)

	bWendu	bWendu	bWendu
ymd
2017-01-31	3.322581	NaN	NaN
2017-02-28	7.642857	3.322581	NaN
2017-03-31	14.129032	7.642857	NaN
2017-04-30	23.700000	14.129032	NaN
2017-05-31	29.774194	23.700000	NaN
2017-06-30	30.966667	29.774194	NaN
2017-07-31	31.612903	30.966667	NaN
2017-08-31	30.129032	31.612903	NaN
2017-09-30	27.866667	30.129032	NaN
2017-10-31	17.225806	27.866667	NaN
2017-11-30	9.566667	17.225806	NaN
2017-12-31	4.483871	9.566667	NaN
2018-01-31	1.322581	4.483871	3.322581
2018-02-28	4.892857	1.322581	7.642857
2018-03-31	14.129032	4.892857	14.129032

# 环比
series_shift1 = df["bWendu"].shift(periods=1)
df["bWendu_way2_huanbi"] = (df["bWendu"]-series_shift1)/series_shift1

# 同比
series_shift2 = df["bWendu"].shift(periods=12)
df["bWendu_way2_tongbi"] = (df["bWendu"]-series_shift2)/series_shift2

df.head(15)

	bWendu	bWendu_way1_huanbi	bWendu_way1_tongbi	bWendu_way2_huanbi	bWendu_way2_tongbi
ymd
2017-01-31	3.322581	NaN	NaN	NaN	NaN
2017-02-28	7.642857	1.300277	NaN	1.300277	NaN
2017-03-31	14.129032	0.848658	NaN	0.848658	NaN
2017-04-30	23.700000	0.677397	NaN	0.677397	NaN
2017-05-31	29.774194	0.256295	NaN	0.256295	NaN
2017-06-30	30.966667	0.040051	NaN	0.040051	NaN
2017-07-31	31.612903	0.020869	NaN	0.020869	NaN
2017-08-31	30.129032	-0.046939	NaN	-0.046939	NaN
2017-09-30	27.866667	-0.075089	NaN	-0.075089	NaN
2017-10-31	17.225806	-0.381849	NaN	-0.381849	NaN
2017-11-30	9.566667	-0.444632	NaN	-0.444632	NaN
2017-12-31	4.483871	-0.531303	NaN	-0.531303	NaN
2018-01-31	1.322581	-0.705036	-0.601942	-0.705036	-0.601942
2018-02-28	4.892857	2.699477	-0.359813	2.699477	-0.359813
2018-03-31	14.129032	1.887685	0.000000	1.887685	0.000000

方法3. pandas.Series.diff

pandas.Series.diff用于新值减去旧值

官方文档：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.diff.html

pd.concat(
    [df["bWendu"], 
     df["bWendu"].diff(periods=1), 
     df["bWendu"].diff(periods=12)],
    axis=1
).head(15)

	bWendu	bWendu	bWendu
ymd
2017-01-31	3.322581	NaN	NaN
2017-02-28	7.642857	4.320276	NaN
2017-03-31	14.129032	6.486175	NaN
2017-04-30	23.700000	9.570968	NaN
2017-05-31	29.774194	6.074194	NaN
2017-06-30	30.966667	1.192473	NaN
2017-07-31	31.612903	0.646237	NaN
2017-08-31	30.129032	-1.483871	NaN
2017-09-30	27.866667	-2.262366	NaN
2017-10-31	17.225806	-10.640860	NaN
2017-11-30	9.566667	-7.659140	NaN
2017-12-31	4.483871	-5.082796	NaN
2018-01-31	1.322581	-3.161290	-2.00
2018-02-28	4.892857	3.570276	-2.75
2018-03-31	14.129032	9.236175	0.00

# 环比
series_diff1 = df["bWendu"].diff(periods=1)
df["bWendu_way3_huanbi"] = series_diff1/(df["bWendu"]-series_diff1)

# 同比
series_diff2 = df["bWendu"].diff(periods=12)
df["bWendu_way3_tongbi"] = series_diff2/(df["bWendu"]-series_diff2)

df.head(15)

	bWendu	bWendu_way1_huanbi	bWendu_way1_tongbi	bWendu_way2_huanbi	bWendu_way2_tongbi	bWendu_way3_huanbi	bWendu_way3_tongbi
ymd
2017-01-31	3.322581	NaN	NaN	NaN	NaN	NaN	NaN
2017-02-28	7.642857	1.300277	NaN	1.300277	NaN	1.300277	NaN
2017-03-31	14.129032	0.848658	NaN	0.848658	NaN	0.848658	NaN
2017-04-30	23.700000	0.677397	NaN	0.677397	NaN	0.677397	NaN
2017-05-31	29.774194	0.256295	NaN	0.256295	NaN	0.256295	NaN
2017-06-30	30.966667	0.040051	NaN	0.040051	NaN	0.040051	NaN
2017-07-31	31.612903	0.020869	NaN	0.020869	NaN	0.020869	NaN
2017-08-31	30.129032	-0.046939	NaN	-0.046939	NaN	-0.046939	NaN
2017-09-30	27.866667	-0.075089	NaN	-0.075089	NaN	-0.075089	NaN
2017-10-31	17.225806	-0.381849	NaN	-0.381849	NaN	-0.381849	NaN
2017-11-30	9.566667	-0.444632	NaN	-0.444632	NaN	-0.444632	NaN
2017-12-31	4.483871	-0.531303	NaN	-0.531303	NaN	-0.531303	NaN
2018-01-31	1.322581	-0.705036	-0.601942	-0.705036	-0.601942	-0.705036	-0.601942
2018-02-28	4.892857	2.699477	-0.359813	2.699477	-0.359813	2.699477	-0.359813
2018-03-31	14.129032	1.887685	0.000000	1.887685	0.000000	1.887685	0.000000