# Mapping ranges of values in pandas dataframe [duplicate]

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### Question :

Mapping ranges of values in pandas dataframe [duplicate]

Apologies if this has been asked before, but I looked extensively without results.

``````import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randint(1,10,10),columns=['a'])

a
0  7
1  8
2  8
3  3
4  1
5  1
6  2
7  8
8  6
9  6
``````

I’d like to create a new column `b` that maps several values of `a` according to some rule, say a=[1,2,3] is 1, a = [4,5,6,7] is 2, a = [8,9,10] is 3. one-to-one mapping is clear to me, but what if I want to map by a list of values or a range?

I tought along these lines…

``````df['b'] = df['a'].map({[1,2,3]:1,range(4,7):2,[8,9,10]:3})
``````

There are a few alternatives.

### Pandas via `pd.cut` / NumPy via `np.digitize`

You can construct a list of boundaries, then use specialist library functions. This is described in @EdChum’s solution, and also in this answer.

### NumPy via `np.select`

``````df = pd.DataFrame(data=np.random.randint(1,10,10), columns=['a'])

criteria = [df['a'].between(1, 3), df['a'].between(4, 7), df['a'].between(8, 10)]
values = [1, 2, 3]

df['b'] = np.select(criteria, values, 0)
``````

The elements of `criteria` are Boolean series, so for lists of values, you can use `df['a'].isin([1, 3])`, etc.

### Dictionary mapping via `range`

``````d = {range(1, 4): 1, range(4, 8): 2, range(8, 11): 3}

df['c'] = df['a'].apply(lambda x: next((v for k, v in d.items() if x in k), 0))

print(df)

a  b  c
0  1  1  1
1  7  2  2
2  5  2  2
3  1  1  1
4  3  1  1
5  5  2  2
6  4  2  2
7  4  2  2
8  9  3  3
9  3  1  1
``````

IIUC you could use `cut` to achieve this:

``````In[33]:
pd.cut(df['a'], bins=[0,3,7,11], right=True, labels=False)+1

Out[33]:
0    2
1    3
2    3
3    1
4    1
5    1
6    1
7    3
8    2
9    2
``````

Here you’d pass the cutoff values to `cut`, and this will categorise your values, by passing `labels=False` it will give them an ordinal value (zero-based) so you just `+1` to them

Here you can see how the cuts were calculated:

``````In[34]:
pd.cut(df['a'], bins=[0,3,7,11], right=True)

Out[34]:
0     (3, 7]
1    (7, 11]
2    (7, 11]
3     (0, 3]
4     (0, 3]
5     (0, 3]
6     (0, 3]
7    (7, 11]
8     (3, 7]
9     (3, 7]
Name: a, dtype: category
Categories (3, interval[int64]): [(0, 3] < (3, 7] < (7, 11]]
``````