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Python (pandas (pivot_table (psc=pd.pivot_table(psc_all, values='…
Python
pandas
pivot_table
psc=pd.pivot_table(psc_all, values='CREDIT', index=['ISDN'],columns=['DATETIME'], aggfunc=np.sum)
vas_pv=pd.pivot_table(vas,values='START_DATE',index=['ISDN'],columns=['NAME'],aggfunc= lambda x:list(x))
read_csv
psc=pd.read_csv('psc202001.csv',encoding='utf8')
merge
df = pd.merge(psc,n10,how='left',on='ISDN')
concat
df = pd.concat([df1,df2, ...,dfn])
groupby
psc_sum=psc.groupby('Datetime').agg(subs_count=('ISDN','count'), credit_sum=('CREDIT',sum))
rename_columns
df.columns = [hdr.replace("str1","str2") for hdr in df.columns)
flattened.columns = [hdr.replace("('pills', ", "strength.").replace(")", "") \
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to_csv
psc.to_csv('psc.csv',encoding='utf8')
drop column
df.drop(['column_name'],axis=1,inplace=True]
n1110["S_TIME"] = pd.to_datetime(n1110["S_TIME"],format="%d/%m/%Y %H:%M:%S",errors='coerce')
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data.drop(['Unnamed: 32'],axis=1,inplace=True)
adc.rename(columns={'msisdn':'isdn'},inplace=True)
df[(df['PSC10']>0)&((pd.datetime.strptime('2019-10-31 00:00:00',"%Y-%m-%d %H:%M:%S") - df['ACTIVEDATE'])>'90 days')].count()
matplotlib_venn
venn2(subsets = (df_count, avb_count,intersec_count), set_labels = ('3k3d_no_vas_no_funring_no_bl', 'avb'))
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