Visualizing Global Land Temperatures in Python with scrapy, xarray, and cartopy

A few years ago, I worked on a project that involved collecting data on a variety of global environmental conditions over time. Some of the data sets included cloud cover, rainfall, types of land cover, sea temperature, and land temperature. I enjoyed developing a greater understanding of our Earth by visualizing how these conditions vary over time around the planet. To get a sense of how fun and informative it can be to analyze environmental data over time, let’s work on visualizing global land surface temperatures from 2001 to 2016.



The data we’ll use in this post are NASA Earth Observation’s Land Science Team’s daytime land surface temperatures, “temperatures of the “skin” (or top 1 millimeter) of the land surface during the daytime, collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument on NASA’s Terra and Aqua satellites”. Temperatures in the data range from -25 ºC (-13 ºF) to 45 ºC (113 ºF).

The data are available at resolutions of 1.0, 0.5, 0.25, and 0.1 degrees. Degrees of latitude are approximately 69 miles (111 kilometers) apart, so the 0.1 degrees files contain land temperature readings spaced approximately 6.9 miles (11.1 km) apart north and south. Unlike latitude, the distance between degrees of longitude varies by latitude. The distance is greatest at the equator and gradually shrinks to zero at the poles. For instance, at the equator, degrees of longitude are approximately 69 miles (111 km) apart; whereas, at 40º north and south, degrees of longitude are approximately 53 miles (85 km) apart. To take advantage of the most fine-grained data available, we’ll use the 0.1 degrees files in this post.


Create Environment

To begin, let’s create a dedicated folder and Python environment for this project. The following commands create a new folder named land_temperature and, inside it, another folder named input_files and then move you into the land_temperature folder:

mkdir -p land_temperature/input_files

cd land_temperature

The following conda commands create and activate a new Python 3.5 environment named land_temp that includes the listed packages, as well as their dependencies. If you’re not using the Anaconda distribution of Python, you can use the venv module in Python 3’s standard library to create a similar dedicated environment:

conda create --name land_temp python=3.5 pandas xarray scrapy matplotlib seaborn cartopy jupyter

source activate land_temp

Create Web Page URLs

Now that we’ve activated our dedicated Python environment, let’s inspect NASA NEO’s land surface temperature web page URL to determine how we’ll need to change it to access all of the web pages we need to visit. The URL for the month-level data for 2001-01-01 is:


If you change the 2001 to 2002 and refresh the page, you’ll see you’re now viewing the month-level data for 2002-01-01. If you make a similar change to the month, you’ll see you’re now viewing data for a different month. It appears we can simply change the date in the URL to access all of the month-level files from 2001 to 2016. Let’s use pandas in the ipython interactive shell to generate this list of URLs:


import pandas as pd

start_date = '2001-01-01'
end_date = '2016-12-01'

dates = pd.date_range(start=start_date, end=end_date, freq='MS')

dates = [dt.strftime('%Y-%m-%d') for dt in dates]

url_base = ""

urls = [url_base+dt for dt in dates]


Inspect Web Page HTML

Now that we have the web page URLs we need, let’s use Chrome’s element inspection tool and the scrapy interactive shell to determine how to extract the links to the data files from the web pages. To start, let’s click on the File Type dropdown menu to see what file types are available. There are several options, but let’s plan to download the type, CSV for Excel.

Below the File Type dropdown menu, there are four geographic resolution options, 1.0, 0.5, 0.25, and 0.1 degrees, which provide increasingly granular data. Let’s right-click on the tiny, right-facing arrow to the right of 0.1 degrees 3600 x 1800 and select Inspect to inspect the HTML near the link in Chrome’s element inspection tool.


The HTML shows us the link to the data file is in a table. Moreover, the link is in a row that has class=”size-option” and, within the data cell (td) element, it is in a hyperlink (a) element’s href attribute. With this understanding of the HTML path to the data file link, let’s use scrapy’s interactive shell to figure out how to extract the link:

scrapy shell ""

response.css('tr.size-option td a::attr(href)').extract()

response.css('tr.size-option td a::attr(href)')[-1].extract()


If you inspect a few of the data file links, you’ll notice an issue with them (i.e. a number in the middle of the URL that varies) that we need to address if we want to download the files programmatically:


In the previous section, when we generated the web page URLs, the portion of the URL that needed to change was the date at the end of the URL, and it needed to change in an understandable way. In this case, I don’t know which number is associated with each URL (and I can’t guess the underlying pattern if there is one), so I can’t generate them programmatically. Instead of generating the data file links like the web page URLs in the previous section, let’s simply scrape the actual data file links from the web pages.

Scrape Data File URLs

Now that we know how to select the data file links, let’s use scrapy to extract them from the web pages so we can then use them to download the data files. In total, there will be 192 URLs and files (12 months per year x 16 years = 192 monthly files).

From inside the land_temperature folder, type the following commands:

scrapy startproject scrape_land_temps

cd scrape_land_temps

Now that we’re inside the first scrape_land_temps folder, let’s create a scrapy spider, a Python file, named inside the scrape_land_temps/spiders folder. In the spider, let’s combine our web page URL generation code with our href link extraction code to instruct the spider to visit each of the 192 month-level web pages and extract the link to the 0.1 degrees data file from each page. Then we can use these URLs to download the CSV files:

import scrapy
import pandas as pd

class LandTempCSVFilesSpider(scrapy.Spider):
        name = "land_temp_csv_files"

        def start_requests(self):
                start_date = '2001-01-01'
                end_date = '2016-12-01'
                dates = pd.date_range(start=start_date, end=end_date, freq='MS')
                dates = [dt.strftime('%Y-%m-%d') for dt in dates]
                url_base = ''
                urls = [url_base+dt for dt in dates]

                for url in urls:
                        yield scrapy.Request(url=url, callback=self.parse)

        def parse(self, response):
                dt = response.url.split("=")[-1]
                url = response.css('tr.size-option td a::attr(href)')[-1].extract()
                url = url.replace('JPEG', 'SS.CSV')
                yield {'date': dt, 'url': url}


Let’s use the following command to run the spider and extract the links to the data files. The result is a JSON file named land_temp_csv_files_urls.json that contains an array of 192 objects, each containing a date and the link to the data file associated with the date:

scrapy crawl land_temp_csv_files -o ../land_temp_csv_files_urls.json

cd ..

Download Data Files

We’re finally ready to download the 192 month-level land surface temperature data files. Let’s return to the ipython interactive shell and use the following code to iterate through the array of URLs in our JSON file to download the CSV files.

First, we read the pairs of dates and URLs in the JSON file into a dataframe named ‘df’. Next, we loop over these pairs (i.e. rows in the dataframe) and, for each one, use the URL to read the remote CSV file into a dataframe named ‘dat’ and then write the dataframe to a local file in the input_files folder.

We insert the date, e.g. 2001-01-01, into the filenames so we know which date each file represents. Also, we use try-except blocks around the reading and writing operations so the loop won’t terminate if it runs into any issues (instead, it will print messages to the screen):


import pandas as pd

df = pd.read_json('land_temp_csv_files_urls.json')

for index, row in df.iterrows():
        print(index, row['url'])
                dat = pd.read_csv(row['url'], header=0, index_col=0)
                print('Error reading: {}'.format(row['url']))

        filename = 'MOD11C1_M_LSTDA_{}_rgb_3600x1800.SS.CSV'.format(row['date'].strftime('%Y-%m-%d'))
                print('Error writing: {}'.format(filename))


Combine Data

Now that we have all of our data files, let’s return to the ipython interactive shell and use the following code to read and combine all of the CSV files into a three-dimensional array (i.e. x = longitude, y = latitude, z = date):

import xarray as xr
import numpy as np
import pandas as pd
import as ccrs
import matplotlib.pyplot as plt
from pathlib import Path

The following code snippet is a helper function we’ll use to make the file-reading code shown below easier to read. This function takes a file name as input, splits it into pieces at the underscores, extracts the piece with index position 3 (this piece is the date, e.g. 2001-01-01), and converts the date into a datetime object:

def date_from_filename(filename):
        fn =
        dt = pd.to_datetime(fn.split('_')[3])
        return dt

The following code snippet is another helper function we’ll use to make the file-reading code easier to read. This function takes an array as input, converts all of the array elements into floating-point numbers, rounds all of the numbers to a specified number of decimal places (the default is 2 decimal places), and then converts the elements to string type:

def round_coords(arr, d=2):
        vals = map(str, [round(float(val), d) for val in arr])
        return vals

The following line of code uses the pathlib module in Python’s standard library to create and return a sorted list of paths to all of the CSV files in the input_files folder:

files = sorted(Path("input_files/").glob("*.CSV"))

The block of code shown below reads all of the CSV files and combines them into a three-dimensional array (i.e. x = longitude, y = latitude, z = date). We’ll use the list named ‘das’ to collect the 192 individual arrays. Later, we’ll pass this list of arrays to xarray’s concat function to concatenate them into a new, combined array. Similarly, we’ll use the list named ‘dts’ to collect the 192 dates so we can use them as the new dimension in the combined array.

Next, we start to loop through each of the CSV files. For each file, we use the date_from_filename function to extract the date from the filename and append it into the dts list. Next, we read the CSV file, noting that the first row is the header row of longitude values, the first column is the index of latitude values, and NA data values are coded as 99999.0. The next three lines round the data, longitude, and latitude values to two decimal places.

Next, we input these values into xarray’s DataArray constructor to create a two-dimensional array and add it to the das list. Finally, we use xarray’s concat function to combine the 192 two-dimensional arrays into a three-dimensional array with the new dimension named ‘date’:

das = []
dts = []
for input_file in files:
        dt = date_from_filename(input_file)
        df = pd.read_csv(input_file, header=0, index_col=0, na_values=99999.0)
        df = df.round(2)
        df.columns = round_coords(df.columns)
        df.index = round_coords(df.index)
        da = xr.DataArray(df.values,
                coords=[[float(lat) for lat in df.index], [float(lon) for lon in df.columns]],
                dims=['latitude', 'longitude'])

da = xr.concat(das, pd.Index(dts, name='date'))


At this point, we should have a three-dimensional array named ‘da’ we can use to analyze and visualize land surface temperatures from 2001 to 2016. Let’s check to make sure the array has the expected dimensions and appears to have the right content:



The temperature values are in degrees Celsius. Nearly everyone in the world learns this temperature scale, except for people in the United States. Since many readers live outside the United States, I’m going to leave the values in degrees Celsius; however, converting them to degrees Fahrenheit is straightforward:

da.values = (da.values * 1.8) + 32
da = da.round(2)

Average Land Surface Temperatures

xarray extends pandas and numpy functionality to facilitate multi-dimensional indexing, grouping, and computing. As an example, we can calculate the average land surface temperatures across all 192 months and display them on a map with the following code:

da.mean(dim='date').plot(figsize=(10, 6));


The PlateCaree projection is a nice default, but let’s explore some other map projections.


Remembering the scene about projections in the television show, The West Wing, here is the same data displayed on a map with the Mollweide projection:

plt.figure(figsize=(10, 6))
ax_p = plt.gca(projection=ccrs.Mollweide(), aspect='auto')
da.mean(dim='date').plot.imshow(ax=ax_p, transform=ccrs.PlateCarree());


As an additional example, the following code block displays the data on a map with the Robinson projection. This example also illustrates some of the additional arguments you can supply to the plot.imshow function to customize the plot:

fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection=ccrs.Robinson(), aspect='auto')
da.mean(dim='date').plot.imshow(ax=ax, transform=ccrs.PlateCarree(),
                x='longitude', y='latitude',
                robust=True, # vmin=-25, vmax=40,


By Specific Geographic Area

The previous examples displayed maps of the entire Earth. In some cases, you may only be interested in a specific segment of the globe. In these cases, you can use array indexing to filter for the subset of data you want or use cartopy’s set_extent function to restrict the map to a specific geographic area.

If you use array indexing, be sure to check the ordering of your array’s axes so you place your index values or ranges in the right positions. For example, in our ‘da’ array the ordered dimensions are date, latitude, and longitude (which we can check with da.shape), so the indexing in the following command selects all dates, latitudes between 20.05 and 50.05, and longitudes between -66.50 and -125.05:

usa = da.loc[:, 50.05:20.05, -125.05:-66.50]


Alternatively, we can use cartopy’s set_extent function to restrict the map to a specific segment of the globe:

plt.figure(figsize=(10, 6))
ax_p = plt.gca(projection=ccrs.LambertConformal(), aspect='auto')
ax_p.set_extent([-125.05, -66.50, 20.05, 50.05])
usa.mean(dim='date').plot.imshow(ax=ax_p, transform=ccrs.PlateCarree());


By Month of the Year

The previous plots calculated average land surface temperatures across all 192 months, which doesn’t let us see temperature differences among months of the year, i.e. January, February, …, December. To calculate average temperatures for each month, we can use xarray’s groupby function to group our data by month of the year and then calculate average temperatures for these groups:

by_month = da.groupby('date')
by_month.plot(x='longitude', y='latitude', col='month', col_wrap=4);


By Season

In xarray’s documentation, Joe Hamman demonstrates how to calculate season averages with weighted averages that account for the fact that months have different numbers of days. Slightly adapting his code for our dataset, we can view how global land surface temperatures vary across seasons (to run the code shown below, you’ll first need to copy and paste Joe’s dpm dictionary and leap_year and get_dpm functions):

month_length = xr.DataArray(get_dpm(, calendar='noleap'),
                coords=[], name='month_length')
weights = month_length.groupby('date.season') / month_length.groupby('date.season').sum()
np.testing.assert_allclose(weights.groupby('date.season').sum().values, np.ones(4))

da_weighted = (da * weights).groupby('date.season').sum(dim='date')

fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(15,4))
for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):
        if i == 3:
                        ax=axes[i], robust=True, cmap='RdBu_r', #'Spectral_r',
                        add_colorbar=True, extend='both')
                        ax=axes[i], robust=True, cmap='RdBu_r',
                        add_colorbar=False, extend='both')

for i, ax in enumerate(axes.flat):
        if i > 0:

axes[0].set_title('Weighted by DPM')


Looping through Months of the Year

The previous examples generated static images. While you can certainly scan over the month of year and season-based plots to inspect differences among the time periods, it can be helpful to generate the plots in a loop so you can focus on a geographic area of interest and let the program handle transitioning from one time period to the next.

Since we’re going to loop over time periods, e.g. months of the year, I’d like to label each plot so we know which time period is being displayed. In our dataset, the months are numbered from 1 to 12. I want to be able to refer to January instead of month 1, so let’s create a dictionary that maps the month number to the corresponding name:

months = {
        1: 'January',
        2: 'February',
        3: 'March',
        4: 'April',
        5: 'May',
        6: 'June',
        7: 'July',
        8: 'August',
        9: 'September',
        10: 'October',
        11: 'November',
        12: 'December'

Next, let’s write a function that will generate each plot, e.g. one for each month of the year. Inside the function, let’s use matplotlib’s clf function to clear the current figure, create a plot axis with the Robinson projection, filter for the subset of arrays with the specified month of the year, create a plot of the average land surface temperatures in the month across all sixteen years, and finally use the name of the month as the plot title:

def plot_monthly(month_number):
        ax_p = plt.gca(projection=ccrs.Robinson(), aspect='auto')
        d = da.loc[ == month_number]
        d.mean(dim='date').plot.imshow(ax=ax_p, transform=ccrs.PlateCarree())

To generate the twelve month-based plots, let’s use matplotlib’s ion and figure functions to turn on matplotlib’s interactive mode and to create an initial figure. Next, let’s establish a for loop to iterate through the integers 1 to 12. As we loop through the integers, we’ll pass each one into our plot_monthly function so it creates a plot based on the data for that month. Since we’re using interactive mode, we need to use matplotlib’s pause function, which pauses the figure for the specified number of seconds, to facilitate the transition behavior. Similarly, we need to use the draw function to update the figure after each transition:

plt.figure(figsize=(10, 6))
for month in range(1,13):



This post demonstrated how to acquire, analyze, and visualize sixteen years’ worth of global land surface temperature data with Python. Along the way, it illustrated ways you can use xarray, matplotlib, and cartopy to select, group, aggregate, and plot multi-dimensional data.

The data set we used in this post required a considerable portion of my laptop’s memory, but it still fit in memory. When the data set you want to use doesn’t fit in your computer’s memory, you may want to consider the Python package, Dask, “a flexible parallel computing library for analytic computing”. Dask extends numpy and pandas functionality to larger-than-memory data collections, such as arrays and data frames, so you can analyze your larger-than-memory data with familiar commands.

Finally, while we focused on land surface temperature data in this post, you can use the analysis and visualization techniques we covered here on other data sets. In fact, you don’t even have to leave the website we relied on for this post, NASA’s NEO website. It offers dozens of other environmental data sets, categorized under atmosphere, energy, land, life, and ocean. This post only scratched the surface of what is possible with xarray and NASA’s data. I look forward to hearing about the cool, or hot, ways you use these resources to study our planet : )


Parsing PDFs in Python with Tika

A few months ago, one of my friends asked me if I could help him extract some data from a collection of PDFs. The PDFs contained records of his financial transactions over a period of years and he wanted to analyze them. Unfortunately, Excel and plain text versions of the files were no longer available, so the PDFs were his only option.

I reviewed a few Python-based PDF parsers and decided to try Tika, which is a port of Apache Tika.  Tika parsed the PDFs quickly and accurately. I extracted the data my friend needed and sent it to him in CSV format so he could analyze it with the program of his choice. Tika was so fast and easy to use that I really enjoyed the experience. I enjoyed it so much I decided to write a blog post about parsing PDFs with Tika.


California Budget PDFs

To demonstrate parsing PDFs with Tika, I knew I’d need some PDFs. I was thinking about which ones to use and remembered a blog post I’d read on scraping budget data from a government website. Governments also provide data in PDF format, so I decided it would be helpful to demonstrate how to parse data from PDFs available on a government website. This way, with these two blog posts, you have examples of acquiring government data, even if it’s embedded in HTML or PDFs. The three PDFs we’ll parse in this post are:

2015-16 State of California Enacted Budget Summary Charts
2014-15 State of California Enacted Budget Summary Charts
2013-14 State of California Enacted Budget Summary Charts


Each of these PDFs contains several tables that summarize total revenues and expenditures, general fund revenues and expenditures, expenditures by agency, and revenue sources. For this post, let’s extract the data on expenditures by agency and revenue sources. In the 2015-16 Budget PDF, the titles for these two tables are:

2015-16 Total State Expenditures by Agency


2015-16 Revenue Sources


To follow along with the rest of this tutorial you’ll need to download the three PDFs and ensure you’ve installed Tika. You can download the three PDFs here:

You can install Tika by running the following command in a Terminal window:

pip install --user tika


Before we dive into parsing all of the PDFs, let’s use one of the PDFs, 2015-16CABudgetSummaryCharts.pdf, to become familiar with Tika and its output. We can use IPython to explore Tika’s output interactively:


from tika import parser

parsedPDF = parser.from_file("2015-16CABudgetSummaryCharts.pdf")

You can type the name of the variable, a period, and then hit tab to view a list of all of the methods available to you:



There are many options related to keys and values, so it appears the variable contains a dictionary. Let’s view the dictionary’s keys:



The dictionary’s keys are metadata and content. Let’s take a look at the values associated with these keys:


The value associated with the key “metadata” is another dictionary. As you’d expect based on the name of the key, its key-value pairs provide metadata about the parsed PDF.


Now let’s take a look at the value associated with “content”.


The value associated with the key “content” is a string. As you’d expect, the string contains the PDF’s text content.


Now that we know the types of objects and values Tika provides to us, let’s write a Python script to parse all three of the PDFs. The script will iterate over the PDF files in a folder and, for each one, parse the text from the file, select the lines of text associated with the expenditures by agency and revenue sources tables, convert each of these selected lines of text into a Pandas DataFrame, display the DataFrame, and create and save a horizontal bar plot of the totals column for the expenditures and revenues. So, after you run this script, you’ll have six new plots, one for revenues and one for expenditures for each of the three PDF files, in the folder in which you ran the script.

Python Script

To parse the three PDFs, create a new Python script named and add the following lines of code:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import glob
import os
import re
import sys
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
pd.options.display.mpl_style = 'default'

from tika import parser

input_path = sys.argv[1]

def create_df(pdf_content, content_pattern, line_pattern, column_headings):
    """Create a Pandas DataFrame from lines of text in a PDF.

    pdf_content -- all of the text Tika parses from the PDF
    content_pattern -- a pattern that identifies the set of lines
    that will become rows in the DataFrame
    line_pattern -- a pattern that separates the agency name or revenue source
    from the dollar values in the line
    column_headings -- the list of column headings for the DataFrame
    list_of_line_items = []
    # Grab all of the lines of text that match the pattern in content_pattern
    content_match =, pdf_content, re.DOTALL)
    # group(1): only keep the lines between the parentheses in the pattern
    content_match =
    # Split on newlines to create a sequence of strings
    content_match = content_match.split('\n')
    # Iterate over each line
    for item in content_match:
        # Create a list to hold the values in the line we want to retain
        line_items = []
        # Use line_pattern to separate the agency name or revenue source
        # from the dollar values in the line
        line_match =, item, re.I)
        # Grab the agency name or revenue source, strip whitespace, and remove commas
        # group(1): the value inside the first set of parentheses in line_pattern
        agency =',', '')
        # Grab the dollar values, strip whitespace, replace dashes with 0.0, and remove $s and commas
        # group(2): the value inside the second set of parentheses in line_pattern
        values_string =\
        replace('- ', '0.0 ').replace('$', '').replace(',', '')
        # Split on whitespace and convert to float to create a sequence of floating-point numbers
        values = map(float, values_string.split())
        # Append the agency name or revenue source into line_items
        # Extend the floating-point numbers into line_items so line_items remains one list
        # Append line_item's values into list_of_line_items to generate a list of lists;
        # all of the lines that will become rows in the DataFrame
    # Convert the list of lists into a Pandas DataFrame and specify the column headings
    df = pd.DataFrame(list_of_line_items, columns=column_headings)
    return df

def create_plot(df, column_to_sort, x_val, y_val, type_of_plot, plot_size, the_title):
    """Create a plot from data in a Pandas DataFrame.

    df -- A Pandas DataFrame
    column_to_sort -- The column of values to sort
    x_val -- The variable displayed on the x-axis
    y_val -- The variable displayed on the y-axis
    type_of_plot -- A string that specifies the type of plot to create
    plot_size -- A list of 2 numbers that specifies the plot's size
    the_title -- A string to serve as the plot's title
    # Create a figure and an axis for the plot
    fig, ax = plt.subplots()
    # Sort the values in the column_to_sort column in the DataFrame
    df = df.sort_values(by=column_to_sort)
    # Create a plot with x_val on the x-axis and y_val on the y-axis
    # type_of_plot specifies the type of plot to create, plot_size
    # specifies the size of the plot, and the_title specifies the title
    df.plot(ax=ax, x=x_val, y=y_val, kind=type_of_plot, figsize=plot_size, title=the_title)
    # Adjust the plot's parameters so everything fits in the figure area
    # Create a PNG filename based on the plot's title, replace spaces with underscores
    pngfile = the_title.replace(' ', '_') + '.png'
    # Save the plot in the current folder

# In the Expenditures table, grab all of the lines between Totals and General Government
expenditures_pattern = r'Totals\n+(Legislative, Judicial, Executive.*?)\nGeneral Government:'

# In the Revenues table, grab all of the lines between 2015-16 and either Subtotal or Total
revenues_pattern = r'\d{4}-\d{2}\n(Personal Income Tax.*?)\n +[Subtotal|Total]'

# For the expenditures, grab the agency name in the first set of parentheses
# and grab the dollar values in the second set of parentheses
expense_pattern = r'(K-12 Education|[a-z,& -]+)([$,0-9 -]+)'

# For the revenues, grab the revenue source in the first set of parentheses
# and grab the dollar values in the second set of parentheses
revenue_pattern = r'([a-z, ]+)([$,0-9 -]+)'

# Column headings for the Expenditures DataFrames
expense_columns = ['Agency', 'General', 'Special', 'Bond', 'Totals']

# Column headings for the Revenues DataFrames
revenue_columns = ['Source', 'General', 'Special', 'Total', 'Change']

# Iterate over all PDF files in the folder and process each one in turn
for input_file in glob.glob(os.path.join(input_path, '*.pdf')):
    # Grab the PDF's file name
    filename = os.path.basename(input_file)
    print filename
    # Remove .pdf from the filename so we can use it as the name of the plot and PNG
    plotname = filename.strip('.pdf')

    # Use Tika to parse the PDF
    parsedPDF = parser.from_file(input_file)
    # Extract the text content from the parsed PDF
    pdf = parsedPDF["content"]
    # Convert double newlines into single newlines
    pdf = pdf.replace('\n\n', '\n')

    # Create a Pandas DataFrame from the lines of text in the Expenditures table in the PDF
    expense_df = create_df(pdf, expenditures_pattern, expense_pattern, expense_columns)
    # Create a Pandas DataFrame from the lines of text in the Revenues table in the PDF
    revenue_df = create_df(pdf, revenues_pattern, revenue_pattern, revenue_columns)
    print expense_df
    print revenue_df

    # Print the total expenditures and total revenues in the budget to the screen
    print "Total Expenditures: {}".format(expense_df["Totals"].sum())
    print "Total Revenues: {}\n".format(revenue_df["Total"].sum())

    # Create and save a horizontal bar plot based on the data in the Expenditures table
    create_plot(expense_df, "Totals", ["Agency"], ["Totals"], 'barh', [20,10], \
    # Create and save a horizontal bar plot based on the data in the Revenues table
    create_plot(revenue_df, "Total", ["Source"], ["Total"], 'barh', [20,10], \

Save this code in a file named in the same folder as the one containing the three CA Budget PDFs. Then you can run the script on the command line with the following command:

./ .

I added docstrings to the two functions, create_df and create_plot, and comments above nearly every line of code in an effort to make the code as self-explanatory as possible. I created the two functions to avoid duplicating code because we perform these operations twice for each file, once for revenues and once for expenditures. We use a for loop to iterate over the PDFs and for each one we extract the lines of text we care about, convert the text into a Pandas DataFrame, display some of the DataFrame’s information, and save plots of the total values in the revenues and expenditures tables.


Terminal Output
(1 of 3 pairs of DataFrames)


PNG File: Expenditures by Agency 2015-16
(1 of 6 PNG Files)


In this post I’ve tried to convey that Tika is a great resource for parsing PDFs by demonstrating how you can use it to parse budget data from PDF documents provided by a government agency. As my friend’s experience illustrates, there may be other situations in which you need to extract data from PDFs. With Tika, PDFs become another rich source of data for your analysis.