Using pandas to summarize and plot data

Question

I will like to use pandas to summarize or visualize some useful summary from my data to highlight how suppliers deviate from the date order was due for supply. Here's a snippet of my data frame:

Supplier    TimeDiff (days)
A   3 days
B   4 days
B   12 days
A   0 days
C   1 days
B   2 days
D   3 days
E   5 days
E   7 days

"Supplier" column contain the supplier codes and "TimeDiff" column contain time difference (date range obtained by deducting "order due date" from "order received date").

Does anyone know how best I can summarise this data? Thanks

Can you be more specific? What do you want to learn from this data? You can for example get average "TimeDiff" for each supplier like this: data.groupby('Supplier').mean()['TimeDiff'] — burhan
– burhan, Commented Nov 1, 2016 at 22:48

Dennis Golomazov · Accepted Answer · 2016-11-02 22:54:42Z

2

I'd start with calculating the mean TimeDiff by supplier:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame([['A', '3 days'], ['B', '4 days'], ['B', '12 days'], ['A', '0 days']], columns=['Supplier', 'TimeDiff'])
df['TimeDiff'] = df['TimeDiff'].str.extract(r'(\d+)').astype(int)
print df.groupby('Supplier').mean()

          TimeDiff
Supplier          
A              1.5
B              8.0

res.plot.bar()
plt.show()

edited Nov 2, 2016 at 22:54

answered Nov 1, 2016 at 22:51

Dennis Golomazov

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1 Comment

user27976 Over a year ago

Thank you guys for these solutions. I wonder if its possible to compute a bar chart directly.

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