Plotting email attachment data using pandas

attachments3.py

import pandas as pd
import numpy as np
import humanfriendly
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import os.path

# Read in our email data file
df = pd.read_csv('../attachments2.csv', header = 0)

# Filter out sent mail 
notFromMe = df.query('FromEmail != "hartez@gmail.com"')

def filetype(row):
    if not(isinstance(row['ContentTypeName'], str)):
        return ''
    if row['MediaSubtype'] == 'octet-stream':
        return os.path.splitext(row['ContentTypeName'])[1]
    return row['MediaSubtype']

# Extract type data from the filename where MediaSubtype doesn't have it
notFromMe['FileType'] = notFromMe.apply(lambda row: filetype(row), axis = 1)

# Group everything by mime type 
types = notFromMe.groupby(['FileType'])

# and get counts and total sizes for each mime type
types = types.agg({'AttachmentId' : 'count', 'Size' : 'sum'})

# Get total amounts so we can calculate percentages later
totalCount = types['AttachmentId'].sum()
totalSize = types['Size'].sum()

# Get the count and divide by the total so we can get
# the percentage for each mime type
types['percentCount'] = types['AttachmentId'] / totalCount
types['percentSize'] = types['Size'] / totalSize

def combinedPlot(df, col, cutoff):
    
    # Just get the mime types which are 1% or more
    overCutoff = df[col]
    overCutoff = df.query(col + '>' + str(cutoff))

    # Fill in the 'other' section
    remaining = 1 - (overCutoff[col].sum())
    other = pd.DataFrame({col : pd.Series([remaining], index = ['other'])})

    # Add the 'other' section to our main data
    both = overCutoff.append(other)

    # Plot it
    both[col].plot(kind='pie', figsize=(6, 6), title='Attachment Types')
    plt.show()

combinedPlot(types, 'percentSize', 0.02)