问题描述
我正在尝试绘制两个数据集的折线图，每个数据集包含值 'date'、'conversions' 和 'cpi' 作为一条线。该图表显示每天的值计数，因此我在处理同一天正确绘制每个数据集的值时遇到问题：
样本数据：
date conversions cpi
0 2020-11-02 0.0 0.000000e+00
1 2020-11-03 0.0 0.000000e+00
2 2020-11-04 0.0 0.000000e+00
3 2020-11-05 0.0 0.000000e+00
4 2020-11-06 3.0 6.333000e-01
5 2020-11-07 0.0 0.000000e+00
6 2020-11-08 0.0 0.000000e+00
7 2020-11-09 0.0 0.000000e+00
8 2020-11-10 0.0 0.000000e+00
9 2020-11-11 2.0 1.695000e+00
10 2020-11-12 0.0 0.000000e+00
11 2020-11-13 2.0 2.170000e+00
12 2020-11-14 0.0 0.000000e+00
13 2020-11-15 1.0 2.590000e+00
14 2020-11-16 2.0 2.670000e+00
0 2020-11-02 5.0 2.039435e+06
1 2020-11-03 6.0 2.788452e+06
2 2020-11-04 8.0 1.720630e+06
3 2020-11-05 8.0 2.038703e+06
4 2020-11-06 11.0 1.775534e+06
5 2020-11-07 14.0 1.810215e+06
6 2020-11-08 30.0 1.617934e+06
7 2020-11-09 27.0 1.784663e+06
8 2020-11-10 32.0 1.368291e+06
9 2020-11-11 4.0 5.293594e+06
10 2020-11-12 17.0 1.524248e+06
11 2020-11-13 20.0 2.437085e+06
12 2020-11-14 24.0 2.272977e+06
13 2020-11-15 38.0 1.848160e+06
14 2020-11-16 22.0 2.415721e+06
我的代码是：
asa_installs_time = get_installs_time(start_date,end_date)
ga_installs_time = get_GAinstalls_time(start_date,end_date)
asa_installsTime_df = pd.DataFrame.from_dict(asa_installs_time[1])
ga_installsTime_df = pd.DataFrame.from_dict(ga_installs_time)
all_installsTime_df = pd.concat([ga_installsTime_df,asa_installsTime_df])
installs_time_series_chart = px.line( all_installsTime_df,x= all_installsTime_df['date'],all_installsTime_df['conversions'],title='Installs per Day')
return [all_installsTime_df]
如何解决绘制两个相同日期的问题？
编辑
使用 all_installsTime_df = all_installsTime_df.sort_values('date').reset_index(drop=True)：
解决方法
- 主要问题是需要
.groupby'date'和.sum()来自两个数据帧的值。
import pandas as pd
import plotly.express as px
# sample data
data1 = {'date': ['2020-11-02','2020-11-03','2020-11-04','2020-11-05','2020-11-06','2020-11-07','2020-11-08','2020-11-09','2020-11-10','2020-11-11','2020-11-12','2020-11-13','2020-11-14','2020-11-15','2020-11-16'],'conversions': [0,3,2,1,2],'cpi': [0.0,0.0,0.6333,1.695,2.17,2.59,2.67]}
data2 = {'date': ['2020-11-02','conversions': [5.0,6.0,8.0,11.0,14.0,30.0,27.0,32.0,4.0,17.0,20.0,24.0,38.0,22.0],'cpi': [2039435.0,2788452.0,1720630.0,2038703.0,1775534.0,1810215.0,1617934.0,1784663.0,1368291.0,5293594.0,1524248.0,2437085.0,2272977.0,1848160.0,2415721.0]}
# create dataframes
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# concat the dataframes
df = pd.concat([df1,df2]).reset_index(drop=True)
# set the date column as a datetime
df.date = pd.to_datetime(df.date)
# groupby date,aggregate sum on all columns and reset
dfg = df.groupby('date').sum().reset_index()
# plot
fig = px.line(dfg,x=dfg['date'],y=dfg['conversions'],title='Installs per Day')
fig.show()
display(dfg)
date conversions cpi
0 2020-11-02 5.0 2.039435e+06
1 2020-11-03 6.0 2.788452e+06
2 2020-11-04 8.0 1.720630e+06
3 2020-11-05 8.0 2.038703e+06
4 2020-11-06 14.0 1.775535e+06
5 2020-11-07 14.0 1.810215e+06
6 2020-11-08 30.0 1.617934e+06
7 2020-11-09 27.0 1.784663e+06
8 2020-11-10 32.0 1.368291e+06
9 2020-11-11 6.0 5.293596e+06
10 2020-11-12 17.0 1.524248e+06
11 2020-11-13 22.0 2.437087e+06
12 2020-11-14 24.0 2.272977e+06
13 2020-11-15 39.0 1.848163e+06
14 2020-11-16 24.0 2.415724e+06