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    <p>Suppose you start with this data:</p> <pre><code>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 </code></pre> <p>Then you can find the status category using <code>pd.cut</code>. Note that by default, <code>pd.cut</code> splits the Series <code>df['Days Late']</code> into categories which are <em>half-open</em> intervals, <code>(-1, 14], (14, 35], (35, 56], (56, 365]</code>:</p> <pre><code>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 </code></pre> <p>Now use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot.html" rel="noreferrer"><code>pivot</code></a> to get the DataFrame in the desired form:</p> <pre><code>df = df.pivot(index='ID', columns='status', values='quantity') </code></pre> <p>and use <code>reindex</code> to obtain the desired order for the rows and columns:</p> <pre><code>df = df.reindex(columns=labels[::-1], index=df.index[::-1]) </code></pre> <hr> <p>Thus, </p> <pre><code>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) </code></pre> <p>yields</p> <pre><code> Red Amber Yellow White ID STRSUB 56 60 74 40 BOTDWG 20 67 87 34 </code></pre>
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    1. COThank you so much for this, I think this is going to help me achieve a lot with PANDAS in my day to day work. Thanks also to mtadd, I notice you have also updated your answer (it's appretiated).
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