Lookup Pandas Dataframe comparing different size data frames - python-3.x

Question

I have two pandas df that look like this
df1
Amount Price
0 5 50
1 10 53
2 15 55
3 30 50
4 45 61
df2
Used amount
0 4.5
1 1.2
2 6.2
3 4.1
4 25.6
5 31
6 19
7 15
I am trying to insert a new column on df2 that will give provide the price from the df1, df1 and df2 have different size, df1 is smaller
I am expecting something like this
df3
Used amount price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31 61
6 19 50
7 15 55
I am thinking to solve this, with something like this function
def price_function(key, table):
used_amount_df2 = (row[0] for row in df1)
price = filter(lambda x: x < key, used_amount_df1)

Answer 1

Here is my own solution
1st approach:
from itertools import product
import pandas as pd
df2=df2.reset_index()
DF=pd.DataFrame(list(product(df2.Usedamount, df1.Amount)), columns=['l1', 'l2'])
DF['DIFF']=(DF.l1-DF.l2)
DF=DF.loc[DF.DIFF<=0,]
DF=DF.sort_values(['l1','DIFF'],ascending=[True,False]).drop_duplicates(['l1'],keep='first')
df1.merge(DF,left_on='Amount',right_on='l2',how='left').merge(df2,left_on='l1',right_on='Usedamount',how='right').loc[:,['index','Usedamount','Price']].set_index('index').sort_index()
Out[185]:
Usedamount Price
index
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55
2nd using pd.merge_asof I recommend this
df2=df2.rename({'Used amount':Amount}).sort_values('Amount')
df2=df2.reset_index()
pd.merge_asof(df2,df1,on='Amount',allow_exact_matches=True,direction='forward')\
.set_index('index').sort_index()
Out[206]:
Amount Price
index
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55

Answer 2

Using pd.IntervalIndex you can
In [468]: df1.index = pd.IntervalIndex.from_arrays(df1.Amount.shift().fillna(0),df1.Amount)
In [469]: df1
Out[469]:
Amount Price
(0.0, 5.0] 5 50
(5.0, 10.0] 10 53
(10.0, 15.0] 15 55
(15.0, 30.0] 30 50
(30.0, 45.0] 45 61
In [470]: df2['price'] = df2['Used amount'].map(df1.Price)
In [471]: df2
Out[471]:
Used amount price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55

Answer 3

You can use cut or searchsorted for create bins.
Notice: Index in df1 has to be default - 0,1,2....
#create default index if necessary
df1 = df1.reset_index(drop=True)
#create bins
bins = [0] + df1['Amount'].tolist()
#get index values of df1 by values of Used amount
a = pd.cut(df2['Used amount'], bins=bins, labels=df1.index)
#assign output
df2['price'] = df1['Price'].values[a]
print (df2)
Used amount price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55
a = df1['Amount'].searchsorted(df2['Used amount'])
df2['price'] = df1['Price'].values[a]
print (df2)
Used amount price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55

Answer 4

You can use pd.DataFrame.reindex with method=bfill
df1.set_index('Amount').reindex(df2['Used amount'], method='bfill')
Price
Used amount
4.5 50
1.2 50
6.2 53
4.1 50
25.6 50
31.0 61
19.0 50
15.0 55
To add that to a new column we can use
join
df2.join(
df1.set_index('Amount').reindex(df2['Used amount'], method='bfill'),
on='Used amount'
)
Used amount Price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55
Or assign
df2.assign(
Price=df1.set_index('Amount').reindex(df2['Used amount'], method='bfill').values)
Used amount Price
0 4.5 50
1 1.2 50
2 6.2 53
3 4.1 50
4 25.6 50
5 31.0 61
6 19.0 50
7 15.0 55