[英]Summing observations from column in pandas
Suppose I have a big Dataframe DS_df w/ column names year, dealamount and CCS among others.假设我有一个大的 Dataframe DS_df 列名年份、交易金额和 CCS 等。 For every year, from 1985 until 2020, I need a separate panda series ie sum_2019.从 1985 年到 2020 年,每一年,我都需要一个单独的熊猫系列,即 sum_2019。 I need to sum the dealamount, if CCS does occur multiple times (if it occurs only once, it should just be added to the series) and the year matches:我需要总结交易金额,如果 CCS 确实发生多次(如果它只发生一次,它应该被添加到系列中)并且年份匹配:
year dealamount CCS
0 2013 37,522,700 Albania_Azerbaijan
1 2013 37,522,700 Albania_Azerbaijan
2 2016 436,341,300 Albania_Greece
3 2019 763,189,200 Albania_Russia
4 2019 763,189,200 Albania_Russia
5 2019 763,189,200 Albania_Russia
6 2019 763,189,200 Albania_Russia
7 2017 150,931,000 Albania_Turkey
8 2016 275,293,750 Albania_Turkey
9 2009 258,328,000 Albania_Turkey
10 2019 153,452,000 Albania_Venezuela
11 2019 153,452,000 Albania_Venezuela
11 2017 153,452,000 Albania_Venezuela
So in this case, sum_2019 should be a panda series w/ the Index being CCS and the summed dealamount as "observation".所以在这种情况下,sum_2019 应该是一个熊猫系列,其中索引为 CCS,总交易量为“观察”。
Albania_Russia 3,052,756,800
Albania_Venezuela 306,904
Likewise, for sum_2013:同样,对于 sum_2013:
Albania_Azerbaijan 75,045,400
Any help is greatly appreciated, as I need to this for quite a lot of data points and feel quite lost (really new to python) How would I go about properly automating this?非常感谢任何帮助,因为我需要很多数据点并且感觉很迷茫(对python来说真的很新)我将如何正确地自动化这个?
Thank you!!谢谢!!
Do you want this?你想要这个吗?
df.dealamount = df.dealamount.str.replace(',','').astype(int)
new_df = df.groupby(['year','CCS']).agg({'dealamount': sum})
Output - Output -
dealamount
year CCS
2009 Albania_Turkey 258328000
2013 Albania_Azerbaijan 75045400
2016 Albania_Greece 436341300
Albania_Turkey 275293750
2017 Albania_Turkey 150931000
Albania_Venezuela 153452000
2019 Albania_Russia 3052756800
Albania_Venezuela 306904000
# to avoid scientific notation (e notation)
pd.set_option('display.float_format', lambda x: '%.d' % x)
# first filter by 'year' then group by 'CSS' and finally sum by 'dealamount'
sum_2019 = df[df['year']==2019].groupby('CCS')['dealamount'].sum()
print(sum_2019)
CCS
Albania_Russia 3052756800
Albania_Venezuela 306904000
Name: dealamount, dtype: float64
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