Grouping data by value ranges

pandas, python-2.7

Solution

Suppose you start with this data:

df = pd.DataFrame({'ID': ('STRSUB BOTDWG'.split())*4,
                   'Days Late': [60, 60, 50, 50, 20, 20, 10, 10],
                   'quantity': [56, 20, 60, 67, 74, 87, 40, 34]})
#    Days Late      ID  quantity
# 0         60  STRSUB        56
# 1         60  BOTDWG        20
# 2         50  STRSUB        60
# 3         50  BOTDWG        67
# 4         20  STRSUB        74
# 5         20  BOTDWG        87
# 6         10  STRSUB        40
# 7         10  BOTDWG        34

Then you can find the status category using `pd.cut`. Note that by default, `pd.cut` splits the Series `df['Days Late']` into categories which are half-open intervals, `(-1, 14], (14, 35], (35, 56], (56, 365]`:

df['status'] = pd.cut(df['Days Late'], bins=[-1, 14, 35, 56, 365], labels=False)
labels = np.array('White Yellow Amber Red'.split())
df['status'] = labels[df['status']]
del df['Days Late']
print(df)
#        ID  quantity  status
# 0  STRSUB        56     Red
# 1  BOTDWG        20     Red
# 2  STRSUB        60   Amber
# 3  BOTDWG        67   Amber
# 4  STRSUB        74  Yellow
# 5  BOTDWG        87  Yellow
# 6  STRSUB        40   White
# 7  BOTDWG        34   White

Now use `pivot` to get the DataFrame in the desired form:

df = df.pivot(index='ID', columns='status', values='quantity')

and use `reindex` to obtain the desired order for the rows and columns:

df = df.reindex(columns=labels[::-1], index=df.index[::-1])

Thus,

import numpy as np
import pandas as pd

df = pd.DataFrame({'ID': ('STRSUB BOTDWG'.split())*4,
                   'Days Late': [60, 60, 50, 50, 20, 20, 10, 10],
                   'quantity': [56, 20, 60, 67, 74, 87, 40, 34]})
df['status'] = pd.cut(df['Days Late'], bins=[-1, 14, 35, 56, 365], labels=False)
labels = np.array('White Yellow Amber Red'.split())
df['status'] = labels[df['status']]
del df['Days Late']
df = df.pivot(index='ID', columns='status', values='quantity')
df = df.reindex(columns=labels[::-1], index=df.index[::-1])
print(df)

yields

        Red  Amber  Yellow  White
ID                               
STRSUB   56     60      74     40
BOTDWG   20     67      87     34

Problem

I have a csv file that shows parts on order. The columns include days late, qty and commodity. I need to group the data by days late and commodity with a sum of the qty. However the days late needs to be grouped into ranges. ``` >56 >35 and <= 56 >14 and <= 35 >0 and <=14 ``` I was hoping I could use a dict some how. Something like this ``` {'Red':'>56,'Amber':'>35 and <= 56','Yellow':'>14 and <= 35','White':'>0 and <=14'} ``` I am looking for a result like this ``` Red Amber Yellow White STRSUB 56 60 74 40 BOTDWG 20 67 87 34 ``` I am new to pandas so I don't know if this is possible at all. Could anyone provide some advice. Thanks

Original source