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loops - 行迭代（两个循环）自动化 Pandas DataFrame

问题描述

根据 df1 中的指标“H”和“G”，我对每一行都有以下迭代（for 循环）。使用选定指标的乘积创建一个新列。现在我希望所有指标都自动下降（如果我有超过“H”和“G”）。不幸的是，我正在努力将其放入字典中。

有人能帮忙吗？谢谢您，祝您度过愉快的一周。

   df1 =pd.DataFrame({'Country':['Armenia','Azerbaidjan','Belarus','Armenia','Azerbaidjan','Belarus'],\
                 'Indictaor':['G','G','G','H', 'H', 'H'],'2005':[3,4,5,6,7,4],'2006':[6,3,1,3,5,6]})

    df2 = pd.DataFrame({'Year':[2005,2006,2005,2006],
                        'Country1':['Armenia','Armenia','Azerbaidjan','Azerbaidjan'],
                        'Country2': ['Belarus','Belarus','Belarus','Belarus']})

    df3 = pd.DataFrame({'Year':[2005,2006,2005,2006],                   
                        'Country2': ['Belarus','Belarus','Belarus','Belarus'],
                        'Country1':['Armenia','Armenia','Azerbaidjan','Azerbaidjan'],
                         'IndictaorGProduct':[15,6,35,5],
                          'IndictaorHProduct':[24,18,28,30]})

    gprod = []
    hprod =[]

for row in df4.iterrows() :
        c1 = row[1][2]
        c2 = row[1][1]
        yr = str(row[1][0])
        g1 = df1.loc[(df1['Country']==c1)&(df1['Indictaor']=='G')]
        g1val = g1[yr].values[0]
        g2 = df1.loc[(df1['Country']==c2)&(df1['Indictaor']=='G')]
        g2val = g2[yr].values[0]
        print(g1val, g2val, g1val*g2val)
        gprod.append(g1val*g2val)
    df4['GProduct'] = gprod

for row in df4.iterrows() :
        c1 = row[1][2]
        c2 = row[1][1]
        yr = str(row[1][0])
        g1 = df1.loc[(df1['Country']==c1)&(df1['Indictaor']=='H')]
        g1val = g1[yr].values[0]
        g2 = df1.loc[(df1['Country']==c2)&(df1['Indictaor']=='H')]
        g2val = g2[yr].values[0]
        print(g1val, g2val, g1val*g2val)
        gprod.append(g1val*g2val)
    df4['HProduct'] = hprod

标签： loopsfor-loopiteration

解决方案

这取决于您从哪里获得指标。您决定它们还是从列中获取它们？

如果您从相应的列中获取它们，您可以使用该列从该列中获取具有唯一值的列表。然后你可以在第二个循环中循环这些值。但请注意，根据您的数据大小，这可能不是很有效。

但是，您可以这样做：

import pandas as pd

df1 = pd.DataFrame({'Country': ['Armenia', 'Azerbaidjan', 'Belarus', 'Armenia', 'Azerbaidjan', 'Belarus'], \
                    'Indictaor': ['G', 'G', 'G', 'H', 'H', 'H'], '2005': [3, 4, 5, 6, 7, 4],
                    '2006': [6, 3, 1, 3, 5, 6]})

df2 = pd.DataFrame({'Year': [2005, 2006, 2005, 2006],
                    'Country1': ['Armenia', 'Armenia', 'Azerbaidjan', 'Azerbaidjan'],
                    'Country2': ['Belarus', 'Belarus', 'Belarus', 'Belarus']})

df3 = pd.DataFrame({'Year': [2005, 2006, 2005, 2006],
                    'Country2': ['Belarus', 'Belarus', 'Belarus', 'Belarus'],
                    'Country1': ['Armenia', 'Armenia', 'Azerbaidjan', 'Azerbaidjan'],
                    'IndictaorGProduct': [15, 6, 35, 5],
                    'IndictaorHProduct': [24, 18, 28, 30]})

cols = ['Year', 'Country2', 'Country1']
df4 = pd.DataFrame(columns=cols)
df4['Year'] = df2['Year']
df4['Country1'] = df2['Country1']
df4['Country2'] = df2['Country2']




indicators = df1["Indictaor"].unique() # get all the unique indicators from the indicators column, you could also manually have alist with the indicators you want to loop over

for i in indicators:
    prod = []
    for row in df4.iterrows():
        c1 = row[1][2]
        c2 = row[1][1]
        yr = str(row[1][0])
        g1 = df1.loc[(df1['Country'] == c1) & (df1['Indictaor'] == i)] # compare to the indicator in the list
        g1val = g1[yr].values[0]
        g2 = df1.loc[(df1['Country'] == c2) & (df1['Indictaor'] == i)]
        g2val = g2[yr].values[0]
        print(g1val, g2val, g1val * g2val)
        prod.append(g1val * g2val)
    colname = "".join([i,"Product"])
    df4[colname] = prod

print("Done")

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