遍历一个数据框中的单个列与另一个数据框中的列进行比较使用熊猫在第一个数据框中创建新列

Question

right now I have two dataframes they look like:

c = pd.DataFrame({'my_goal':[3, 4, 5, 6, 7],
                 'low_number': [0,100,1000,2000,3000],
                 'high_number': [100,1000,2000,3000,4000]})

and

a= pd.DataFrame({'a':['a', 'b', 'c', 'd', 'e'],
                'Number':[50, 500, 1030, 2005 , 3575]})

what I want to do is if 'Number' falls between the low number and the high number I want it to bring back the value in 'my_goal'. For example if we look at 'a' it's 'Number is is 100 so I want it to bring back 3. I also want to create a dataframe that contains all the columns from dataframe a and the 'my_goal' column from dataframe c. I want the output to look like:

I tried making my high and low numbers into a separate list and running a for loop from that, but all that gives me are 'my_goal' numbers:

low_number= 'low_number': [0,100,1000,2000,3000]
for i in a:
    if float(i) >= low_number:
        a = c['my_goal']

print(a)

Answer 1

You can use pd.cut , when I see ranges, I first think of pd.cut:

dfa = pd.DataFrame(a)
dfc = pd.DataFrame(c)

dfa['my_goal'] = pd.cut(dfa['Number'],
                        bins=[0]+dfc['high_number'].tolist(),
                        labels=dfc['my_goal'])

Output:

   a  Number my_goal
0  a      50       3
1  b     500       4
2  c    1030       5
3  d    2005       6
4  e    3575       7

Answer 2

I changed row 4 slightly to include a test case where the condition is not met. You can concat a with rows of c where the condition is true.

a= pd.DataFrame({'a':['a', 'b', 'c', 'd', 'e'],'Number':[50, 500, 1030, 1995 , 3575]})
cond= a.Number.between( c.low_number, c.high_number)

pd.concat([a, c.loc[cond, ['my_goal']] ], axis = 1, join = 'inner')


    Number  a   my_goal
0   50      a   3
1   500     b   4
2   1030    c   5
4   3575    e   7