Populate column in data frame based on a range found in another dataframe

Question

I\'m attempting to populate a column in a data frame based on whether the index value of that record falls within a range defined by two columns in another data frame.

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Answer 1

import pandas as pd
import numpy as np

# Here is your existing dataframe
df_existing = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))

# Create a new empty dataframe with specific column names and data types
df_new = pd.DataFrame(index=None)
columns = ['field01','field02','field03','field04']
dtypes = [str,int,int,int]
for c,d in zip(columns, dtypes):
    df_new[c] = pd.Series(dtype=d)

# Set the index on the new dataframe to same as existing 
df_new['new_index'] = df_existing.index
df_new.set_index('new_index', inplace=True)

# Fill the new dataframe with specific fields from the existing dataframe
df_new[['field02','field03']] = df_existing[['B','C']]
print df_new

Answer 2

Alternative solution:

---

classdict = df2.set_index("CLASS").to_dict("index")

rangedict = {}

for key,value in classdict.items():

    # get all items in range and assign value (the key)
    for item in list(range(value["START"],value["STOP"]+1)):
        rangedict[item] = key

extract rangedict:

{2: 1, 3: 1, 5: 2, 6: 2, 7: 2, 8: 3}

now map and possibly format(?):

df1['CLASS'] = df1.index.to_series().map(rangedict)
df1.applymap("{0:.0f}".format)

outputs:

a   CLASS
0   4   nan
1   45  nan
2   7   1
3   5   1
4   48  nan
5   44  2
6   22  2
7   89  2
8   45  3
9   44  nan
10  23  nan

Answer 3

You can use IntervalIndex (requires v0.20.0).

First construct the index:

df2.index = pd.IntervalIndex.from_arrays(df2['START'], df2['STOP'], closed='both')

df2
Out: 
        START  STOP  CLASS
[2, 3]      2     3      1
[5, 7]      5     7      2
[8, 8]      8     8      3

Now if you index into the second DataFrame it will lookup the value in the intervals. For example,

df2.loc[6]
Out: 
START    5
STOP     7
CLASS    2
Name: [5, 7], dtype: int64

returns the second class. I don't know if it can be used with merge or with merge_asof but as an alternative you can use map:

df1['CLASS'] = df1.index.to_series().map(df2['CLASS'])

Note that I first converted the index to a Series to be able to use the Series.map method. This results in

df1
Out: 
     a  CLASS
0    4    NaN
1   45    NaN
2    7    1.0
3    5    1.0
4   48    NaN
5   44    2.0
6   22    2.0
7   89    2.0
8   45    3.0
9   44    NaN
10  23    NaN