我想遍历Multiindex，并将“ 2017-01-01”当月第一天的“ Supply”列（对于前10个“ Symbols”，已经排名）乘以“ Price”（相同的10个符号），直到月底为止，然后将值存储在新列“ New.Cap”中，并对每个月重复相同的操作。
我尝试了for循环，但是无法正常工作。我能够获得前10名的“供应”值，然后乘以第二天（相同“符号”的价格）并将其存储为“ 2017-01-02”
我的数据框：
Date Symbol Market.Cap Price Circulating.Supply
1/1/2017 BTC 16050414526 998.33 16077337
1/1/2017 ETH 715049311 8.17 87493800
1/1/2017 XRP 231408729 0.01 36337298649
1/1/2017 LTC 221718486 4.51 49144154
1/1/2017 XMR 190983552 13.97 13668252
1/1/2017 ETC 122202804 1.4 87444118
1/1/2017 DASH 78524020 11.23 6992839
1/1/2017 REP 43994860 4 11000000
1/1/2017 MAID 43862003 0.1 452552412
1/1/2017 STEEM 36999610 0.16 229582096
1/1/2017 XEM 30962258 0 8999999999
1/1/2017 ICN 26381302 0.3 87000000
1/1/2017 FCT 25392986 2.9 8753219
1/1/2017 DOGE 24501946 0 1.07544E+11
1/1/2017 WAVES 22132166 0.22 100000000
1/1/2017 DGD 18079172 9.04 2000000
1/2/2017 BTC 16429072864 1021.75 16079300
1/2/2017 ETH 733331862 8.38 87525306
1/2/2017 XRP 229312859 0.01 36337298649
1/2/2017 LTC 228536428 4.65 49158829
1/2/2017 XMR 219509938 16.05 13675283
1/2/2017 ETC 125743676 1.44 87476792
1/2/2017 DASH 82595831 11.81 6994957
1/2/2017 REP 46101566 4.19 11000000
1/2/2017 MAID 42320210 0.09 452552412
1/2/2017 STEEM 35374447 0.15 229645428
1/2/2017 XEM 30396875 0 8999999999
1/2/2017 ICN 26451766 0.3 87000000
1/2/2017 FCT 26119449 2.98 8753219
1/2/2017 DOGE 24343443 0 1.07557E+11
1/2/2017 WAVES 23267904 0.23 100000000
1/2/2017 ZEC 17773124 49.79 356994
1/3/2017 BTC 16786354619 1043.84 16081362
1/3/2017 ETH 851511659 9.73 87556829
1/3/2017 XRP 232046528 0.01 36337298649
1/3/2017 LTC 227483720 4.63 49172954
1/3/2017 XMR 220176407 16.09 13681607
1/3/2017 ETC 130608332 1.49 87508802
1/3/2017 DASH 89434687 12.78 6997071
1/3/2017 REP 46955890 4.27 11000000
1/3/2017 MAID 44863660 0.1 452552412
1/3/2017 STEEM 36308540 0.16 229753160
1/3/2017 XEM 32157836 0 8999999999
1/3/2017 ICN 29520641 0.34 87000000
1/3/2017 FCT 27402737 3.13 8753219
1/3/2017 DOGE 24429805 0 1.07571E+11
1/3/2017 WAVES 23992972 0.24 100000000
索引列是前两个（日期和符号）。
这是我到目前为止所拥有的：
df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date')
df = df.set_index('Symbol', append=True)
s = df['Circulating.Supply'].xs('2017-01-01') * df['Price'].xs('2017-01- 02')
s.index = [np.repeat(pd.Timestamp('2017-01-02'), len(s)), s.index]
df['New.Cap'] = s
print(df['New.Cap'].loc['2017-01-02'])
答案 0 :(得分：0)
无需进入日期时间索引，这将适用于您提供的数据。请注意，如果一个月以上，则可能需要使用DateTimeIndex（也许使用.astype('datetime64[M]')）与每个月的第一天进行比较：
s = pd.Series(
pd.concat([df.loc[pd.to_datetime('2017-01-01')]['Circulating.Supply'] *
df.loc[date]['Price']
for date in df.index.get_level_values('Date').unique()])
.dropna())
df['New.Cap'] = s.values
答案 1 :(得分：0)
您可以使用：
#get top10 rows per datetimes in DatetimeIndex
df = df.groupby(level=0).head(10)
#create MultiIndex
df1 = df.set_index('Symbol', append=True)
#print (df1)
#subtract one day and create MultiIndex
df2 = df.set_index(df.index - pd.Timedelta(1, unit='d')).set_index('Symbol', append=True)
#print (df2)
#multiple both Series from first and second DataFrame
df1['new'] = df1['Circulating.Supply'] * df2['Price']
print (df1)
Market.Cap Price Circulating.Supply new
Date Symbol
2017-01-01 BTC 16050414526 998.33 1.607734e+07 1.642702e+10
ETH 715049311 8.17 8.749380e+07 7.331980e+08
XRP 231408729 0.01 3.633730e+10 3.633730e+08
LTC 221718486 4.51 4.914415e+07 2.285203e+08
XMR 190983552 13.97 1.366825e+07 2.193754e+08
ETC 122202804 1.40 8.744412e+07 1.259195e+08
DASH 78524020 11.23 6.992839e+06 8.258543e+07
REP 43994860 4.00 1.100000e+07 4.609000e+07
MAID 43862003 0.10 4.525524e+08 4.072972e+07
STEEM 36999610 0.16 2.295821e+08 3.443731e+07
2017-01-02 BTC 16429072864 1021.75 1.607930e+07 1.678422e+10
ETH 733331862 8.38 8.752531e+07 8.516212e+08
XRP 229312859 0.01 3.633730e+10 3.633730e+08
LTC 228536428 4.65 4.915883e+07 2.276054e+08
XMR 219509938 16.05 1.367528e+07 2.200353e+08
ETC 125743676 1.44 8.747679e+07 1.303404e+08
DASH 82595831 11.81 6.994957e+06 8.939555e+07
REP 46101566 4.19 1.100000e+07 4.697000e+07
MAID 42320210 0.09 4.525524e+08 4.525524e+07
STEEM 35374447 0.15 2.296454e+08 3.674327e+07
2017-01-03 BTC 16786354619 1043.84 1.608136e+07 NaN
ETH 851511659 9.73 8.755683e+07 NaN
XRP 232046528 0.01 3.633730e+10 NaN
LTC 227483720 4.63 4.917295e+07 NaN
XMR 220176407 16.09 1.368161e+07 NaN
ETC 130608332 1.49 8.750880e+07 NaN
DASH 89434687 12.78 6.997071e+06 NaN
REP 46955890 4.27 1.100000e+07 NaN
MAID 44863660 0.10 4.525524e+08 NaN
STEEM 36308540 0.16 2.297532e+08 NaN
编辑：
每个月的第一天获取前10个值，以unstack重塑形状并将索引设置为月周期：
df1 = (df.groupby(df.index.to_period('m'))
.head(10)
.set_index('Symbol', append=True)['Circulating.Supply']
.unstack())
df1.index = df1.index.to_period('m').rename('month')
#print (df1)
重塑类似内容并创建带有月周期的MultiIndex：
df2 = df.set_index('Symbol', append=True)['Price'].unstack()
df2.index = [df2.index, df2.index.to_period('m').rename('month')]
#print (df2)
按月划分，删除助手级别，重新调整形状，最后添加新列：
s = df2.div(df1, level=1).reset_index(level=1, drop=True).stack().rename('new')
df = df.join(s, on=['Date','Symbol'])
#print (df)