[英]Pandas iterate over rows and find the column names
i have a two dataframes as: 我有两个数据帧:
df = pd.DataFrame({'America':["Ohio","Utah","New York"],
'Italy':["Rome","Milan","Venice"],
'Germany':["Berlin","Munich","Jena"]});
df2 = pd.DataFrame({'Cities':["Rome", "New York", "Munich"],
'Country':["na","na","na"]})
i want to itirate on df2 "Cities" column to find the cities on my (df) and append the country of the city (df column names) to the df2 country column 我想在df2“城市”列上找到我的(df)上的城市,并将城市的国家/地区(df列名称)附加到df2国家/地区列
Use melt
with map
by dictionary: 通过字典使用
map
melt
:
df1 = df.melt()
print (df1)
variable value
0 America Ohio
1 America Utah
2 America New York
3 Italy Rome
4 Italy Milan
5 Italy Venice
6 Germany Berlin
7 Germany Munich
8 Germany Jena
df2['Country'] = df2['Cities'].map(dict(zip(df1['value'], df1['variable'])))
#alternative, thanks @Sandeep Kadapa
#df2['Country'] = df2['Cities'].map(df1.set_index('value')['variable'])
print (df2)
Cities Country
0 Rome Italy
1 New York America
2 Munich Germany
After melting and renaming the first dataframe: 融化并重命名第一个数据帧后:
df1 = df.melt().rename(columns={'variable': 'Country', 'value': 'Cities'})
the solution is a simple merge: 解决方案是一个简单的合并:
df2 = df2[['Cities']].merge(df1, on='Cities')
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