Using MapQuest API to Get Geo Data

A friendly tutorial on getting zip codes and other geographic data from street addresses.


Knowing how to deal with geographic data is a must-have for a data scientist. In this post, we will play around with the MapQuest Search API to get zip codes from street addresses along with their corresponding latitude and longitude to boot!

The Scenario

In 2019, my friends and I participated in CivTechSA Datathon. At one point in the competition, we wanted to visualize the data points and overlay them on San Antonio’s map. The problem is, we had incomplete data. Surprise! All we had were a street number and a street name — no zip code, no latitude, nor longitude. We then turned to the great internet for some help.

We found a great API by MapQuest that will give us exactly what we needed. With just a sprinkle of Python code, we were able to accomplish our goal.

Today, we’re going to walk through this process.

The Data

To follow along, you can download the data from here. Just scroll down to the bottom tab on over to the Data Catalog 2019. Look for SAWS (San Antonio Water System) as shown below.

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Screenshot by Ednalyn C. De Dios

Download the file by clicking on the link to the Excel file.

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Screenshot by Ednalyn C. De Dios

OR, you can click on this.

MapQuest API Key

Head on over to https://developer.mapquest.com/ and create an account to get a free API key.

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Screenshot by Ednalyn C. De Dios
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Screenshot by Ednalyn C. De Dios
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Screenshot by Ednalyn C. De Dios
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Screenshot by Ednalyn C. De Dios
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Screenshot by Ednalyn C. De Dios

Copy the ‘Consumer Key’ and keep it in a safe place. We’ll need it later.

Jupyter Notebook

Now, let’s fire up a Jupyter notebook and get coding!

For starters, let’s set up the environment by doing a couple of imports.https://towardsdatascience.com/media/7d0f7ced4082761e995ecf8ce0213c3f

Don’t forget to replace the API_KEY (line#12) with your own key above.

Now. let’s read the Excel file with a simple df = pd.read_excel().

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Screenshot by Ednalyn C. De Dios

Next, we’ll combine the street number and street name columns.https://towardsdatascience.com/media/1696465db27770b7f2942ab707d2efa5

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Screenshot by Ednalyn C. De Dios

The ALL CAPS hurts my eyes. Let’s do something about it:

df['street_address'] = df.street_address.str.title() .
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Screenshot by Ednalyn C. De Dios

Below are two functions that call the API and returns geo data.https://towardsdatascience.com/media/3ec6009e8b6069387a9edde18bdad0d3

We can manually call it with the line below. Don’t forget to replace the ‘#####’ with your own API key. You can use any address you want (replace spaces with a + character).

get_zip('https://www.mapquestapi.com/geocoding/v1/address?key=####################&inFormat=kvp&outFormat=json&location=100+ Military+Plaza&thumbMaps=false&delimiter=%2C')

But we’ve got many addresses, so we’ll use a loop to call the API repeatedly.https://towardsdatascience.com/media/9a970862f0352997417c5211df359a9b

Let’s see what the result looks like:

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Screenshot by Ednalyn C. De Dios

Finally, let’s create a dataframe that will house the street addresses — complete with zip code, latitude, and longitude.https://towardsdatascience.com/media/adfcc23ff94f54877bc80b72e2537ed9

Voila! We’ve got ourselves geo data.

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Screenshot by Ednalyn C. De Dios

For extra credit, let’s import the data in Tableau and get a pretty spiffy visual:

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Screenshot by Ednalyn C. De Dios

And that’s it, folks!

You can find the jupyter notebook here.

Thanks for stopping by and reading my post. Hope it was useful 🙂

If you want to learn more about my journey from slacker to data scientist, check out the article below:From Slacker to Data ScientistMy journey into data science without a degree.towardsdatascience.com

And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now:The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.towardsdatascience.com

Stay tuned!

You can reach me on Twitter or LinkedIn.

This article was first published in Towards Data Science’ Medium publication.

Exploring the Trump Twitter Archive with PyCaret

For adventurous beginners in NLP.


For this project, we’ll be using PyCaret:

PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within seconds in your choice of notebook environment.¹

PyCaret

PyCaret does a lot more than NLP. It also does a whole slew of both supervised and unsupervised ML including classification, regression, clustering, anomaly detection, and associate rule mining.

To learn more, check out Moez Ali’s announcement.


Housekeeping

Let’s begin by installing PyCaret. Just do pip install pycaret and we are good to go! Note: PyCaret is a big library so you may want to go grab a cup of coffee while waiting for it to install.

Also, we need to download the English language model because it is not automatically downloaded with PyCaret:

python -m spacy download en_core_web_sm
python -m textblob.download_corpora

Getting the Data

Let’s read the data into a dataframe. If you want to follow along, you can download the dataset here. This dataset contains Trump’s tweets from the moment he took office on January 20, 2017 to May 30, 2020.

import pandas as pd
from pycaret.nlp import *
df = pd.read_csv('trump_20200530.csv')

Let’s check the shape of our data first:

df.shape

And let’s take a quick look:

df.head()

For expediency, let’s sample only 1,000 tweets.

# sampling the data to select only 1000 tweets
df = df.sample(1000, random_state=493).reset_index(drop=True)
df.shape

Topic Modeling

The fun part!

nlp = setup(data = df, target = 'text', session_id = 493,
customI _stopwords = [ 'rt', 'https', 'http', 'co', 'amp'])

PyCaret’s setup() function performs the following text-processing steps:

  1. Removing Numeric Characters
  2. Removing Special Characters
  3. Word Tokenization
  4. Stopword Removal
  5. Bigram Extraction
  6. Trigram Extraction
  7. Lemmatizing
  8. Custom Stopwords

And all in one line of code!

It takes in two parameters: the dataframe in data and the name of the text column that we want to pass in target. In our case, we also used the optional parameters session_id for reproducibility and custom_stopwords to reduce the noise coming from the tweets.

After all is said and done, we’ll get something similar to this:

In the next step, we’ll create the model and we’ll use ‘lda’:

lda = create_model('lda', num_topics = 6, multi_core = True)

Above, we created an ‘lda’ model and passed in the number of topics as 6 and set it so that the LDA will use all CPU cores available to parallelize and speed up training.

Finally, we’ll assign topic proportions to the rest of the dataset using assign_model().

lda_results = assign_model(lda)
lda_results.head()

Visualizing the Results

Let’s the plot the overall frequency distribution of the entire corpus:

plot_model()

Now let’s extract the bigrams and trigrams for the entire corpus:

plot_model(plot = 'bigram')
plot_model(plot = 'trigram')

But what if we only want to extract the n-grams from a specific topic? Easy, we’ll just pass in the topic_num parameter.

plot_model(lda, plot = 'trigram', topic_num = 'Topic 1')

If we want the distribution of topics we’ll simply change it and specify it in the plot parameter.

plot_model(lda, plot = 'topic_distribution')

And that’s it!

We’ve successfully conducted topic modeling on President Trump’s tweets since taking office.

Bonus Round

Moez Ali wrote a great tutorial on using PyCaret in Power BI. Check it out.


Thank you for reading! Exploratory data analysis uses a lot of techniques and we’ve only explored a few on this post. I encourage you to keep practicing and employ other techniques to derive insights from data.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1] PyCaret. (June 4, 2020). Why PyCarethttps://pycaret.org/

This article was first published in Towards Data Science’ Medium publication.

Programming Environment Setup

I was bored over the weekend so I decided to restore my Macbook Pro to factory settings so that I can set up my programming environment the proper way. After all, what’s a data scientist without her toys?

Let’s start with a replacement to the default terminal and pyenv installation to manage different Python versions.

Let’s move on to managing different Python interpreters and virtual environments using pyenv-virtualenv.

Into the Heart of Darkness - Pt. 2

Exploring the Trump Twitter Archive with spaCy.


In a previous post, we set out to explore the dataset provided by the Trump Twitter Archive. My initial goal was to do something fun by using a very interesting dataset. However, it didn’t quite turn out that way.

On this post, we’ll continue our journey but this time we’ll be using spaCy.


For this project, we’ll be using pandas for data manipulation, spaCy for natural language processing, and joblib to speed things up.

Let’s get started by firing up a Jupyter notebook!

Housekeeping

Let’s import pandas and also set the display options so Jupyter won’t truncate our columns and rows. Let’s also set a random seed for reproducibility.

# for manipulating data
import pandas as pd
# setting the random seed for reproducibility
import random
random.seed(493)
# to print out all the outputs
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# set display options
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', -1)

Getting the Data

Let’s read the data into a dataframe. If you want to follow along, you can download the cleaned dataset here along with the file for stop words¹. This dataset contains Trump’s tweets from the moment he took office on January 20, 2017 to May 30, 2020.

df = pd.read_csv('trump_20200530_clean.csv', parse_dates=True, index_col='datetime')

Let’s take a quick look at the data.

df.head()
df.info()

Using spaCy

Now let’s import spaCy and begin natural language processing.

# for natural language processing: named entity recognition
import spacy
import en_core_web_sm

We’re only going to use spaCy’s ner functionality or named-entity recognition so we’ll disable the rest of the functionalities. This will save us a lot of loading time later.

nlp = spacy.load(‘en_core_web_sm’, disable=[‘tagger’, ‘parser’, ‘textcat’])

Now let’s load the contents stopwords file into the variable stopswords. Note that we converted the list into a set to also save some processing time later.

with open(‘twitter-stopwords — TA — Less.txt’) as f:
contents = f.read().split(‘,’)
stopwords = set(contents)

Next, we’ll import joblib and define a few functions to help with parallel processing.

from joblib import Parallel, delayed

def chunker(iterable, total_length, chunksize):
    return (iterable[pos: pos + chunksize] for pos in range(0, total_length, chunksize))

def flatten(list_of_lists):
    "Flatten a list of lists to a combined list"
    return [item for sublist in list_of_lists for item in sublist]

def process_chunk(texts):
    preproc_pipe = []
    for doc in nlp.pipe(texts, batch_size=20):
        preproc_pipe.append([(ent.text) for ent in doc.ents if ent.label_ in ['NORP', 'PERSON', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT']])
    return preproc_pipe

def preprocess_parallel(texts, chunksize=100):
    executor = Parallel(n_jobs=7, backend='multiprocessing', prefer="processes")
    do = delayed(process_chunk)
    tasks = (do(chunk) for chunk in chunker(texts, len(df), chunksize=chunksize))
    result = executor(tasks)
    return flatten(result)

In the code above², the function preprocess_parallel executes the other function process_chunks in parallel to help with speed. The function process_chunks iterates through a series of texts — in our case, the column 'tweet' of our the df dataframe — and inspects the entity if it belongs to either NORP, PERSON, FAC, ORG, GPE, LOC, PRODUCT, or EVENT. If it is, the entity is then appended to 'preproc_pipe' and subsequently returned to its caller. Prashanth Rao has a very good article on making spaCy super fast.

Let’s call the main driver for the functions now.

df['entities'] = preprocess_parallel(df['tweet'], chunksize=1000)

Doing a quick df.head() will reveal the new column 'entities' that we added earlier to hold the entities found in the 'tweet' column.

Prettifying the Results

In the code below, we’re making a list of lists called 'entities' and then flattening it for easier processing. We’re also converting it into a set called 'entities_set'.

entities = [entity for entity in df.entities if entity != []]
entities = [item for sublist in entities for item in sublist]
entities_set = set(entities)

Next, let’s count the frequency of the entities and append it to the list of tuples entities_counts. Then let’s convert the results into a dataframe df_counts.

df_counts = pd.Series(entities).value_counts()[:20].to_frame().reset_index()
df_counts.columns=['entity', 'count']
df_counts

For this step, we’re going to reinitialize an empty list entity_counts and manually construct a list of tuples with a combined set of entities and the sum of their frequencies or count.

entity_counts = []

entity_counts.append(('Democrats', df_counts.loc[df_counts.entity.isin(['Democrats', 'Dems', 'Democrat'])]['count'].sum()))
entity_counts.append(('Americans', df_counts.loc[df_counts.entity.isin(['American', 'Americans'])]['count'].sum()))
entity_counts.append(('Congress', df_counts.loc[df_counts.entity.isin(['House', 'Senate', 'Congress'])]['count'].sum()))
entity_counts.append(('America', df_counts.loc[df_counts.entity.isin(['U.S.', 'the United States', 'America'])]['count'].sum()))
entity_counts.append(('Republicans', df_counts.loc[df_counts.entity.isin(['Republican', 'Republicans'])]['count'].sum()))

entity_counts.append(('China', 533))
entity_counts.append(('FBI', 316))
entity_counts.append(('Russia', 313))
entity_counts.append(('Fake News', 248))
entity_counts.append(('Mexico', 213))
entity_counts.append(('Obama', 176))

Let’s take a quick look before continuing.

Finally, let’s convert the list of tuples into a dataframe.

df_ner = pd.DataFrame(entity_counts, columns=["entity", "count"]).sort_values('count', ascending=False).reset_index(drop=True)

And that’s it!

We’ve successfully created a ranking of the named entities that President Trump most frequently talked about in his tweets since taking office.


Thank you for reading! Exploratory data analysis uses a lot of techniques and we’ve only explored a few on this post. I encourage you to keep practicing and employ other techniques to derive insights from data.

In the next post, we shall continue our journey into the heart of darkness and do some topic-modeling using LDA.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1] GONG Wei’s Homepage. (May 30, 2020). Stop words for tweets. https://sites.google.com/site/iamgongwei/home/sw

[2] Towards Data Science. (May 30, 2020). Turbo-charge your spaCy NLP pipeline. https://towardsdatascience.com/turbo-charge-your-spacy-nlp-pipeline-551435b664ad

Into the Heart of Darkness - Pt. 1

Exploring the Trump Twitter Archive with Python. For beginners.


In this post, we’ll explore the dataset provided by the Trump Twitter Archive. My goal was to do something fun by using a very interesting dataset. However, as it turned out, exposure to Trump’s narcissism and shenanigans were quite depressing — if not traumatic.

You’d been warned!


For this project, we’ll be using pandas and numpy for data manipulation, matplotlib for visualizations, datetime for working with timestamps, unicodedata and regex for processing strings, and finally, nltk for natural language processing.

Let’s get started by firing up a Jupyter notebook!

Environment

We’re going to import pandas and matplotlib, and also set the display options for Jupyter so that the rows and columns are not truncated.

# for manipulating data
import pandas as pd
import numpy as np
# for visualizations
%matplotlib inline
import matplotlib.pyplot as plt
# to print out all the outputs
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# set display options
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', -1)

Getting the Data

Let’s read the data into a dataframe. If you want to follow along, you can download the dataset here. This dataset contains Trump’s tweets from the moment he took office on January 20, 2017 to May 30, 2020.

df = pd.read_csv('trump_20200530.csv')

Let’s look at the first five rows and see the number of records (rows) and fields (columns).

df.head()
df.shape

Let’s do a quick renaming of the columns to make it easier for us later.

df.columns=['source', 'tweet', 'date_time', 'retweets', 'favorites', 'is_retweet', 'id']

Let’s drop the id column since it’s not really relevant right now.

df = df.drop(columns=['id'])

Let’s do a quick sanity check, this time let’s also check the dtypes of the columns.

df.head()
df.info()

Working with Timestamps

We can see from the previous screenshot that the ‘date_time’ column is a string. Let’s parse it to a timestamp.

# for working with timestamps
from datetime import datetime
from dateutil.parser import parse
dt = []
for ts in df.date_time:
dt.append(parse(ts))
dt[:5]

Let’s add a column with ‘datetime’ that contains the timestamp information.

df['datetime'] = df.apply(lambda row: parse(row.date_time), axis=1)

Let’s double-check the data range of our dataset.

df.datetime.min()
df.datetime.max()

Trimming the Data

Let’s see how many sources there are for the tweets.

df.source.value_counts()

Let’s only keep the ones that were made using the ‘Twitter for iPhone’ app.

df = df.loc[df.source == 'Twitter for iPhone']

We should drop the old ‘date_time’ column and the ‘source’ column as well.

df = df.drop(columns=['date_time', 'source'])

Separating the Retweets

Let’s see how many are retweets.

df.is_retweet.value_counts()

Let’s make another dataframe that contains only retweets and drop the ‘is_retweet’ column.

df_retweets = df.loc[df.is_retweet == True]
df_retweets = df_retweets.drop(columns=['is_retweet'])

Sanity check:

df_retweets.head()
df_retweets.shape

Back on the original dataframe, let’s remove the retweets from the dataset and drop the ‘is_retweet’ column altogether.

df = df.loc[df.is_retweet == False]
df = df.drop(columns=['is_retweet'])

Another sanity check:

df.head()
df.shape

Exploring the Data

Let’s explore both of the dataframes and answer a few questions.

What time does the President tweet the most? What time does he tweet the least?

The graph below shows that the President most frequently tweets around 12pm. He also tweets the least around 8am. Maybe he’s not a morning person?

title = 'Number of Tweets by Hour'
df.tweet.groupby(df.datetime.dt.hour).count().plot(figsize=(12,8), fontsize=14, kind='bar', rot=0, title=title)
plt.xlabel('Hour')
plt.ylabel('Number of Tweets')

What day does the President tweet the most? What day does he tweet the least?

The graph below shows that the President most frequently tweets on Wednesday. He also tweets the least on Thursday.

title = 'Number of Tweets by Day of the Week'
df.tweet.groupby(df.datetime.dt.dayofweek).count().plot(figsize=(12,8), fontsize=14, kind='bar', rot=0, title=title)
plt.xlabel('Day of the Week')
plt.ylabel('Number of Tweets')
plt.xticks(np.arange(7),['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])

Isolating Twitter Handles from the Retweets

Let’s import regex so we can use it to parse the text and isolate the Twitter handles of the original tweets. In the code below, we add another column that contains the Twitter handle.

import re
pattern = re.compile('(?<=RT @).*?(?=:)')
df_retweets['original'] = [re.search(pattern, tweet).group(0) for tweet in df_retweets.tweet]

Let’s create another dataframe that will contain only the original Twitter handles and their associated number of retweets.

df_originals = df_retweets.groupby(['original']).sum().sort_values('retweets').reset_index().sort_values('retweets', ascending=False)

Let’s check the data real quick:

df_originals.head()
df_originals.shape

Let’s visualize the results real quick so we can get an idea if the data is disproportionate or not.

df_originals = df_retweets.groupby(['original']).sum().sort_values('retweets').reset_index().sort_values('retweets', ascending=False)[:10].sort_values('retweets')
df_originals.plot.barh(x='original', y='retweets', figsize=(16,10), fontsize=16)
plt.xlabel("Originating Tweet's Username")
plt.xticks([])

Which Twitter user does the President like to retweet the most?

The graph below shows that the President likes to retweet the tweets from ‘@realDonaldTrump’. Does this mean the president likes to retweet himself? You don’t say!

The interesting handle on this one is ‘@charliekirk11’. Charlie Kirk is the founder of Turning Point USA. CBS News described the organization as a far-right organization that is “shunned or at least ignored by more established conservative groups in Washington, but embraced by many Trump supporters”.¹

The Top 5 Retweets

Let’s look at the top 5 tweets that were retweeted the most by others based on the original Twitter handle.

Let’s start with the ones with ‘@realDonaldTrump’.

df_retweets.loc[df_retweets.original == 'realDonaldTrump'].sort_values('retweets', ascending=False)[:5]

And another one with ‘@charliekirk11’.

df_retweets.loc[df_retweets.original == 'charliekirk11'].sort_values('retweets', ascending=False)[:5]

Examining Retweets’ Favorites count

Let’s find out how many of the retweets are favorited by others.

df_retweets.favorites.value_counts()

Surprisingly, none of the retweets seemed to have been favorited by anybody. Weird.

We should drop it.

Counting N-Grams

To do some n-gram ranking, we need to import unicodedata and nltk. We also need to specify additional stopwords that we may need to exclude from our analysis.

# for cleaning and natural language processing
import unicodedata
import nltk
# add appropriate words that will be ignored in the analysis
ADDITIONAL_STOPWORDS = ['rt']

Here are a few of my favorite functions for natural language processing:

def clean(text):
  """
  A simple function to clean up the data. All the words that
  are not designated as a stop word is then lemmatized after
  encoding and basic regex parsing are performed.
  """
  wnl = nltk.stem.WordNetLemmatizer()
  stopwords = nltk.corpus.stopwords.words('english') + ADDITIONAL_STOPWORDS
  text = (unicodedata.normalize('NFKD', text)
    .encode('ascii', 'ignore')
    .decode('utf-8', 'ignore')
    .lower())
  words = re.sub(r'[^\w\s]', '', text).split()
  return [wnl.lemmatize(word) for word in words if word not in stopwords]

def get_words(df, column):
    """
    Takes in a dataframe and columns and returns a list of
    words from the values in the specified column.
    """
    return clean(''.join(str(df[column].tolist())))

def get_bigrams(df, column):
    """
    Takes in a list of words and returns a series of
    bigrams with value counts.
    """
    return (pd.Series(nltk.ngrams(get_words(df, column), 2)).value_counts())[:10]

def get_trigrams(df, column):
    """
    Takes in a list of words and returns a series of
    trigrams with value counts.
    """
    return (pd.Series(nltk.ngrams(get_words(df, column), 3)).value_counts())[:10]

def viz_bigrams(df ,column):
    get_bigrams(df, column).sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8))

    plt.title('20 Most Frequently Occuring Bigrams')
    plt.ylabel('Bigram')
    plt.xlabel('# Occurances')

def viz_trigrams(df, column):
    get_trigrams(df, column).sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8))

    plt.title('20 Most Frequently Occuring Trigrams')
    plt.ylabel('Trigram')
    plt.xlabel('# Occurances')
 

Let’s look at the top 10 bigrams in the df dataframe using the ‘tweet’ column.

get_bigrams(df, 'tweet')

And now, for the top 10 trigrams:

Let’s use the viz_bigrams() function and visualize the bigrams.

viz_bigrams(df, ‘tweet’)

Similarly, let’s use the viz_trigrams() function and visualize the trigrams.

viz_trigrams(df, 'tweet')

And there we have it!

From the moment that Trump took office, we can confidently say that the “fake news media” has been on top of the president’s mind.

Conclusion

Using basic Python and the nltk library, we’ve explored the dataset from the Trump Twitter Archive and did some n-gram ranking out of it.


Thank you for reading! Exploratory data analysis uses a lot of techniques and we’ve only explored a few on this post. I encourage you to keep practicing and employ other techniques to derive insights from data.

In the next post, we shall continue our journey into the heart of darkness and use spaCy to extract named-entities from the same dataset.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1] CBS News. “Trump speaks to conservative group Turning Point USA”. www.cbsnews.com. Archived from the original on July 31, 2019. Retrieved August 5, 2019.

Populating a Network Graph with Named-Entities

An early attempt of using networkx to visualize the results of natural language processing.


I do a lot of natural language processing and usually, the results are pretty boring to the eye. When I learned about network graphs, it got me thinking, why not use keywords as nodes and connect them together to create a network graph?

Yupp, why not!

In this post, we’ll do exactly that. We’re going to extract named-entities from news articles about coronavirus and then use their relationships to connect them together in a network graph.


A Brief Introduction

Network graphs are a cool visual that contains nodes (vertices) and edges (lines). It’s often used in social network analysis and network analysis but data scientists also use it for natural language processing.

Photo by Anders Sandberg on Flicker

Natural Language Processing or NLP is a branch of artificial intelligence that deals with programming computers to process and analyze large volumes of text and derive meaning out of them.¹ In other words, it’s all about teaching computers how to understand human language… like a boss!

Photo by brewbooks on Flickr

Enough introduction, let’s get to coding!


To get started, let’s make sure to take care of all dependencies. Open up a terminal and execute the following commands:

pip install -U spacy
python -m spacy download en
pip install networkx
pip install fuzzywuzzy

This will install spaCy and download the trained model for English. The third command installs networkx. This should work for most systems. If it doesn’t work for you, check out the documentation for spaCy and networkx. Also, we’re using fuzzywuzzy for some text preprocessing.

With that out of the way, let’s fire up a Jupyter notebook and get started!


Imports

Run the following code block into a cell to get all the necessary imports into our Python environment.

import pandas as pd
import numpy as np
import pickle
from operator import itemgetter
from fuzzywuzzy import process, fuzz# for natural language processing
import spacy
import en_core_web_sm# for visualizations
%matplotlib inline
from matplotlib.pyplot import figureimport networkx as nx

Getting the Data

If you want to follow along, you can download the sample dataset here. The file was created using newspaper to import news articles from the npr.org. If you’re feeling adventurous, use the code snippet below to build your own dataset.

import requests
import json
import time
import newspaper
import pickle

npr = newspaper.build('https://www.npr.org/sections/coronavirus-live-updates')

corpus = []
count = 0
for article in npr.articles:
    time.sleep(1)
    article.download()
    article.parse()
    text = article.text
    corpus.append(text)
    if count % 10 == 0 and count != 0:
        print('Obtained {} articles'.format(count))
    count += 1

corpus300 = corpus[:300]

with open("npr_coronavirus.txt", "wb") as fp:   # Pickling
    pickle.dump(corpus300, fp)

# with open("npr_coronavirus.txt", "rb") as fp:   # Unpickling
#     corpus = pickle.load(fp)

Let’s get our data.

with open('npr_coronavirus.txt', 'rb') as fp:   # Unpickling
corpus = pickle.load(fp)

Extract Entities

Next, we’ll start by loading spaCy’s English model:

nlp = en_core_web_sm.load()

Then, we’ll extract the entities:

entities = []for article in corpus[:50]:
tokens = nlp(''.join(article))
gpe_list = []
for ent in tokens.ents:
if ent.label_ == 'GPE':
gpe_list.append(ent.text)
entities.append(gpe_list)

In the above code block, we created an empty list called entities to store a list of lists that contains the extracted entities from each of the articles. In the for-loop, we looped through the first 50 articles of the corpus. For each iteration, we converted each articles into tokens (words) and then we looped through all those words to get the entities that are labeled as GPE for countries, states, and cities. We used ent.text to extract the actual entity and appended them one by one to entities.

Here’s the result:

Note that North Carolina has several variations of its name and some have “the” prefixed in their names. Let’s get rid of them.

articles = []for entity_list in entities:
cleaned_entity_list = []
for entity in entity_list:
cleaned_entity_list.append(entity.lstrip('the ').replace("'s", "").replace("’s",""))
articles.append(cleaned_entity_list)

In the code block above, we’re simply traversing the list of lists articles and cleaning the entities one by one. With each iteration, we’re stripping the prefix “the” and getting rid of 's.

Optional: FuzzyWuzzy

Looking at the entities, I’ve noticed that there are also variations in the “United States” is represented. There exists “United States of America” while some are just “United States”. We can trim these down into a more standard naming convention.

FuzzyWuzzy can help with this.

Described by pypi.org as “string matching like a boss,” FiuzzyWuzzy uses Levenshtein distance to calculate the similarities between words.¹ For a really good tutorial on how to use FuzzyWuzzy, check out Thanh Huynh’s article.FuzzyWuzzy: Find Similar Strings within one column in PythonToken Sort Ratio vs. Token Set Ratiotowardsdatascience.com

Here’s the optional code for using FuzzyWuzzy:

choices = set([item for sublist in articles for item in sublist])

cleaned_articles = []
for article in articles:
    article_entities = []
    for entity in set(article):
        article_entities.append(process.extractOne(entity, choices)[0])
    cleaned_articles.append(article_entities)

For the final step before creating the network graph, let’s get rid of the empty lists within our list of list that were generated by articles who didn’t have any GPE entity types.

articles = [article for article in articles if article != []]

Create the Network Graph

For the next step, we’ll create the world into which the graph will exist.

G = nx.Graph()

Then, we’ll manually add the nodes with G.add_nodes_from().

for entities in articles:
G.add_nodes_from(entities)

Let’s see what the graph looks like with:

figure(figsize=(10, 8))
nx.draw(G, node_size=15)

Next, let’s add the edges that will connect the nodes.

for entities in articles:
if len(entities) > 1:
for i in range(len(entities)-1):
G.add_edges_from([(str(entities[i]),str(entities[i+1]))])

For each iteration of the code above, we used a conditional that will only entertain a list of entities that has two or more entities. Then, we manually connect each of the entities with G.add_edges_from().

Let’s see what the graph looks like now:

figure(figsize=(10, 8))
nx.draw(G, node_size=10)

This graph reminds me of spiders! LOL.

To organize it a bit, I decided to use the shell version of the network graph:

figure(figsize=(10, 8))
nx.draw_shell(G, node_size=15)

We can tell that some nodes are heavier on connections than others. To see which nodes have the most connections, let’s use G.degree().

G.degree()

This gives the following degree view:

Let’s find out which node or entity has the most number of connections.

max(dict(G.degree()).items(), key = lambda x : x[1])

To find out which other nodes have the most number of connections, let’s check the top 5:

degree_dict = dict(G.degree(G.nodes()))
nx.set_node_attributes(G, degree_dict, 'degree')sorted_degree = sorted(degree_dict.items(), key=itemgetter(1), reverse=True)

Above, sorted_degrees is a list that contains all the nodes and their degree values. We only wanted the top 5 like so:

print("Top 5 nodes by degree:")
for d in sorted_degree[:5]:
print(d)

Bonus Round: Gephi

Gephi is an open-source and free desktop application that lets us visualize, explore, and analyze all kinds of graphs and networks.²

Let’s export our graph data into a file so we can import it into Gephi.

nx.write_gexf(G, "npr_coronavirus_GPE_50.gexf")

Cool beans!

Next Steps

This time, we only processed 50 articles from npr.org. What would happen if we processed all 300 articles from our dataset? What will we see if we change the entity type from GPE to PERSON? How else can we use network graphs to visualize natural language processing results?

There’s always more to do. The possibilities are endless!


I hope you enjoyed today’s post. The code is not perfect and we have a long way to go towards realizing insights from the data. I encourage you to dive deeper and learn more about spaCynetworkxfuzzywuzzy, and even Gephi.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1]: Wikipedia. (May 25, 2020). Natural language processing https://en.wikipedia.org/wiki/Natural_language_processing

[2]: Gephi. (May 25, 2020). The Open Graph Viz Platform https://gephi.org/

This article was first published in Towards Data Science‘ Medium publication.

From DataFrame to Named-Entities

A quick-start guide to extracting named-entities from a Pandas dataframe using spaCy.


A long time ago in a galaxy far away, I was analyzing comments left by customers and I noticed that they seemed to mention specific companies much more than others. This gave me an idea. Maybe there is a way to extract the names of companies from the comments and I could quantify them and conduct further analysis.

There is! Enter: named-entity-recognition.

Named-Entity Recognition

According to Wikipedia, named-entity recognition or NER “is a subtask of information extraction that seeks to locate and classify named entity mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.”¹ In other words, NER attempts to extract words that categorized into proper names and even numerical entities.

In this post, I’ll share the code that will let us extract named-entities from a Pandas dataframe using spaCy, an open-source library provides industrial-strength natural language processing in Python and is designed for production use.²

To get started, let’s install spaCy with the following pip command:

pip install -U spacy

After that, let’s download the pre-trained model for English:

python -m spacy download en

With that out of the way, let’s open up a Jupyter notebook and get started!

Imports

Run the following code block into a cell to get all the necessary imports into our Python environment.

# for manipulating dataframes
import pandas as pd# for natural language processing: named entity recognition
import spacy
from collections import Counter
import en_core_web_sm
nlp = en_core_web_sm.load()# for visualizations
%matplotlib inline

The important line in this block is nlp = en_core_web_sm.load() because this is what we’ll be using later to extract the entities from the text.

Getting the Data

First, let’s get our data and load it into a dataframe. If you want to follow along, download the sample dataset here or create your own from the Trump Twitter Archive.

df = pd.read_csv('ever_trump.csv')

Running df.head() in a cell will get us acquainted with the data set quickly.

Getting the Tokens

Second, let’s create tokens that will serve as input for spaCy. In the line below, we create a variable tokens that contains all the words in the 'text' column of the df dataframe.

tokens = nlp(''.join(str(df.text.tolist())))

Third, we’re going to extract entities. We can just extract the most common entities for now:

items = [x.text for x in tokens.ents]
Counter(items).most_common(20)
Screenshot by Author

Extracting Named-Entities

Next, we’ll extract the entities based on their categories. We have a few to choose from people to events and even organizations. For a complete list of all that spaCy has to offer, check out their documentation on named-entities.

Screenshot by Author

To start, we’ll extract people (real and fictional) using the PERSON type.

person_list = []for ent in tokens.ents:
if ent.label_ == 'PERSON':
person_list.append(ent.text)

person_counts = Counter(person_list).most_common(20)df_person = pd.DataFrame(person_counts, columns =['text', 'count'])

In the code above, we started by making an empty list with person_list = [].

Then, we utilized a for-loop to loop through the entities found in tokens with tokens.ents. After that, we made a conditional that will append to the previously created list if the entity label is equal to PERSON type.

We’ll want to know how many times a certain entity of PERSON type appears in the tokens so we did with person_counts = Counter(person_list).most_common(20). This line will give us the top 20 most common entities for this type.

Finally, we created the df_person dataframe to store the results and this is what we get:

Screenshot by Author

We’ll repeat the same pattern for the NORP type which recognizes nationalities, religious and political groups.

norp_list = []for ent in tokens.ents:
if ent.label_ == 'NORP':
norp_list.append(ent.text)

norp_counts = Counter(norp_list).most_common(20)df_norp = pd.DataFrame(norp_counts, columns =['text', 'count'])

And this is what we get:

Screenshot by Author

Bonus Round: Visualization

Let’s create a horizontal bar graph of the df_norp dataframe.

df_norp.plot.barh(x='text', y='count', title="Nationalities, Religious, and Political Groups", figsize=(10,8)).invert_yaxis()
Screenshot by Author

Voilà, that’s it!


I hope you enjoyed this one. Natural language processing is a huge topic but I hope that this gentle introduction will encourage you to explore more and expand your repertoire.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1]: Wikipedia. (May 22, 2020). Named-entity recognition https://en.wikipedia.org/wiki/Named-entity_recognition

[2]: spaCy. (May 22, 2020). Industrial-Strength Natural Language Processing in Python https://spacy.io/

This article was first published in Towards Data Science‘ Medium publication.