initial_month = datetime.strptime('01-2018', '%m-%Y')
final_month = datetime.strptime('12-2020', '%m-%Y')
ppl_initial_comms = ppc_initial_comms = 6174
initial_leadpcpm = 4
ppl_price = 40
ppc_price = 400
ppl_new_comms = ppc_new_comms = 0
growth_years = ['2018','2019','2020']
leadpcpm_rate_dict = {}
ppl_rate_dict = {}
ppc_rate_dict = {}
ppl_cumul_rev_dict = {}
ppc_cumul_rev_dict = {}
#For Loop to calculate yearly MoM rates
for year in growth_years:
if year == '2018':
leadpcpm_rate_dict[year] = 5
ppl_rate_dict[year] = 6
ppc_rate_dict[year] = 0.9 * ppl_rate_dict[year]
elif year == '2019':
leadpcpm_rate_dict[year] = 4
ppl_rate_dict[year] = 4
ppc_rate_dict[year] = 0.9 * ppl_rate_dict[year]
elif year == '2020':
leadpcpm_rate_dict[year] = 1
ppl_rate_dict[year] = 2
ppc_rate_dict[year] = 0.9 * ppl_rate_dict[year]
#While loop to calculate MoM revenue growth over 3 years
while(initial_month != final_month+relativedelta(months=1)):
initial_year = str(initial_month.year)
if initial_year in growth_years:
ppl_new_leadpcpm = initial_leadpcpm + ((initial_leadpcpm*leadpcpm_rate_dict[initial_year]) / 100)
initial_leadpcpm = ppl_new_leadpcpm
ppl_new_comms = ppl_initial_comms + ((ppl_initial_comms*ppl_rate_dict[initial_year]) / 100)
ppl_initial_comms = ppl_new_comms
ppl_cumul_rev = ppl_new_comms * ppl_new_leadpcpm * ppl_price
ppl_cumul_rev_dict[initial_month] = ppl_cumul_rev
ppc_new_comms = ppc_initial_comms + ((ppc_initial_comms*ppc_rate_dict[initial_year]) / 100)
ppc_initial_comms = ppc_new_comms
ppc_cumul_rev = ppc_new_comms * ppc_price
ppc_cumul_rev_dict[initial_month] = ppc_cumul_rev
initial_month += relativedelta(months=1)
我试图用MatPlotLib在一个单线图(时间序列)中可视化36个月内ppl和ppc的收入总和。但是我不知道如何将ppl_cumul_rev_dict
和ppc_cumul_rev_dict
的结果解析成这样的数据帧:
Year PPLRevenue PPCRevenue
0 Jan 2018 1234 5678
1 Feb 2018 9112 10019
.. .. ..
35 Dec 2020 1000000 1500000
我试过创建两个关于ppl和ppc收入的字典,但我不知道如何将它们组合成一个数据帧来输入plt.plot
您可以简单地将dict转换成
pandas
系列,然后创建一个数据帧重写构建词典的方式可能更好,但是,考虑到您提供给我们的代码以及
ppl_cumul_rev_dict
和ppc_cumul_rev_dict
的内容:相关问题 更多 >
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