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Keeping track of which movies I want to watch

One thing I like to do with my family is watch movies. But not just any movies, Comic Book movies. We’ve seen both Thor and Thor: The Dark World, Iron Man and Guardians of the Galaxy. It’s not a lot, but we’re working on it.

I’ve mapped out the Marvel Cinematic Universe movies for us to watch, and it’s OK, but there wasn’t a easy way to link into the iTunes store from the list.

I decided that I could probably use Workflow to do this, but I hadn’t really worked with it to do it, but today I had a bit of time and figured, “what they heck … why not?”

My initial attempt was clunky. It required to workflows to accomplish what I needed. This was because I had to split the work of Workflow into 2 workflows:

  • Get the Name
  • Get the Link

Turns out there’s a much easier way, so I’ll post the link to that workflow, and not the workflows that are much harder to use!

The workflow Add Movie to Watch accepts iTunes products. The workflow then does the following:

  • It saves the iTunes products URL as a variable called iTunes
  • It then gets the iTunes variable to retrieve the Name and sets the value to a variable called Movie
  • Next it asks ‘Who is the movie being added by?’ This is important for my family as we want a common list, but it’s also good to know who added the movie!
  • This value is saved to a variable called User
  • Finally, I want to know when the movie was added so I get the current date.

We take all of the items and construct a bit of text that looks like this:


[{Movie}]({iTunes}) - Added on {Input} by {User}

Where each of the words above surrounded by the {} are the variable names previously mentioned ({Input} is from the get current date and doesn’t need to be saved to a variable).

In my last step I take this text and append it to a file in Dropbox called Movies to Watch.md.

It took way longer than I would have liked to finish this up, but at the end of the day, I’m glad that I was able to get it done.

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Dropbox Files Word Cloud

In one of my previous posts I walked through how I generated a wordcloud based on my most recent 20 tweets. I though it would be neat to do this for my Dropbox file names as well. just to see if I could.

When I first tried to do it (as previously stated, the Twitter Word Cloud post was the first python script I wrote) I ran into some difficulties. I didn’t really understand what I was doing (although I still don’t really understand, I at least have a vague idea of what the heck I’m doing now).

The script isn’t much different than the Twitter word cloud. The only real differences are:

  1. the way in which the words variable is being populated
  2. the mask that I’m using to display the cloud

In order to go get the information from the file system I use the glob library:

import glob

The next lines have not changed

import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
from scipy.misc import imread

Instead of writing to a ‘tweets’ file I’m looping through the files, splitting them at the / character and getting the last item (i.e. the file name) and appending it to the list f:

f = []
for filename in glob.glob('/Users/Ryan/Dropbox/Ryan/**/*', recursive=True):
    f.append(filename.split('/')[-1])

The rest of the script generates the image and saves it to my Dropbox Account. Again, instead of using a Twitter logo, I’m using a Cloud image I found here

words = ' '
for line in f:
    words= words + line

stopwords = {'https'}

logomask = imread('mask-cloud.png')

wordcloud = WordCloud(
    font_path='/Users/Ryan/Library/Fonts/Inconsolata.otf',
    stopwords=STOPWORDS.union(stopwords),
    background_color='white',
    mask = logomask,
    max_words=1000,
    width=1800,
    height=1400
).generate(words)

plt.imshow(wordcloud.recolor(color_func=None, random_state=3))
plt.axis('off')
plt.savefig('/Users/Ryan/Dropbox/Ryan/Post Images/dropbox_wordcloud.png', dpi=300)
plt.show()

And we get this:

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Installing the osmnx package for Python

I read about a cool gis package for Python and decided I wanted to play around with it. This post isn’t about any of the things I’ve learned about the package, it’s so I can remember how I installed it so I can do it again if I need to. The package is described by it’s author in his post

To install osmnx I needed to do the following:

  1. Install Home Brew if it’s not already installed by running this command (as an administrator) in the terminal:

    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

  2. Use Home Brew to install the spatialindex dependency. From the terminal (again as an administrator):

    brew install spatialindex

  3. In python run pip to install rtree:

    pip install rtree

  4. In python run pip to install osmnx

    pip install osmnx

I did this on my 2014 iMac but didn’t document the process. This lead to a problem when I tried to run some code on my 2012 MacBook Pro.

Step 3 may not be required, but I’m not sure and I don’t want to not have it written down and then wonder why I can’t get osmnx to install in 3 years when I try again!

Remember, you’re not going to remember what you did, so you need to write it down!

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Twitter Word Cloud

As previously mentioned I’m a bit of a Twitter user. One of the things that I came across, actually the first python project I did, was writing code to create a word cloud based on the most recent 20 posts of my Twitter feed.

I used a post by Sebastian Raschka and a post on TechTrek.io as guides and was able to generate the word cloud pretty easily.

As usual, we import the need libraries:

import tweepy, json, random
from tweepy import OAuthHandler
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
from scipy.misc import imread

The code below allows access to my feed using secret keys from my twitter account. They have been removed from the post so that my twitter account doesn’t stop being mine:

consumer_key = consumer_key
consumer_secret = consumer_secret
access_token = access_token
access_secret = access_secret

auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)

api = tweepy.API(auth)

Next I open a file called tweets and write to it the tweets (referred to in the for loop as status) and encode with utf-8. If you don’t do the following error is thrown: TypeError: a bytes-like object is required, not 'str'. And who wants a TypeError to be thrown?

f = open('tweets', 'wb')

for status in api.user_timeline():
    f.write(api.get_status(status.id).text.encode("utf-8"))
f.close()

Now I’m ready to do something with the tweets that I collected. I read the file into a variable called words

words=' '
count =0
f = open('tweets', 'rb')
for line in f:
    words= words + line.decode("utf-8")
f.close

Next, we start on constructing the word cloud itself. We declare words that we want to ignore (in this case https is ignored, otherwise it would count the protocol of links that I’ve been tweeting).

stopwords = {'https', 'co', 'RT'}

Read in the twitter bird to act as a mask

logomask = imread('twitter_mask.png')

Finally, generate the wordcloud, plot it and save the image:

wordcloud = WordCloud(
    font_path='/Users/Ryan/Library/Fonts/Inconsolata.otf',
    stopwords=STOPWORDS.union(stopwords),
    background_color='white',
    mask = logomask,
    max_words=500,
    width=1800,
    height=1400
).generate(words)

plt.imshow(wordcloud.recolor(color_func=None, random_state=3))
plt.axis('off')
plt.savefig('./Twitter Word Cloud - '+time.strftime("%Y%m%d")+'.png', dpi=300)
plt.show()

The second to last line generates a dynamically named file based on the date so that I can do this again and save the image without needing to do too much thinking.

Full Code can be found on my GitHub Report

My Twitter Word Cloud as of today looks like this:

I think it will be fun to post this image every once in a while, so as I remember, I’ll run the script again and update the Word Cloud!

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Home, End, PgUp, PgDn … BBEdit Preferences

As I’ve been writing up my posts for the last couple of days I’ve been using the amazing macOS Text Editor BBEdit. One of the things that has been tripping me up though are my ‘Windows’ tendencies on the keyboard. Specifically, my muscle memory of the use and behavior of the Home, End, PgUp and PgDn keys. The default behavior for these keys in BBEdit are not what I needed (nor wanted). I lived with it for a couple of days figuring I’d get used to it and that would be that.

While driving home from work today I was listening to ATP Episode 196 and their Post-Show discussion of the recent departure of Sal Soghoian who was the Project Manager for the macOS automation. I’m not sure why, but suddenly it clicked with me that I could probably change the behavior of the keys through the Preferences for the Keyboard (either system wide, or just in the Application).

When I got home I fired up BBEdit and jumped into the preferences and saw this:

I made a couple of changes, and now the keys that I use to navigate through the text editor are now how I want them to be:

Nothing too fancy, or anything, but goodness, does it feel right to have the keys work the way I need them to.

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Pitching Stats and Python

I’m an avid Twitter user, mostly as a replacement RSS feeder, but also because I can’t stand Facebook and this allows me to learn about really important world events when I need to and to just stay isolated with my head in the sand when I don’t. It’s perfect for me.

One of the people I follow on Twitter is Dr. Drang who is an Engineer of some kind by training. He also appears to be a fan of baseball and posted an analysis of Jake Arrieata’s pitching over the course of the 2016 MLB season (through September 22 at least).

When I first read it I hadn’t done too much with Python, and while I found the results interesting, I wasn’t sure what any of the code was doing (not really anyway).

Since I had just spent the last couple of days learning more about BeautifulSoup specifically and Python in general I thought I’d try to do two things:

  1. Update the data used by Dr. Drang
  2. Try to generalize it for any pitcher

Dr. Drang uses a flat csv file for his analysis and I wanted to use BeautifulSoup to scrape the data from ESPN directly.

OK, I know how to do that (sort of ¯\(ツ)/¯)

First things first, import your libraries:

import pandas as pd
from functools import partial
import requests
import re
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
from datetime import datetime, date
from time import strptime

The next two lines I stole borrowed directly from Dr. Drang’s post. The first line is to force the plot output to be inline with the code entered in the terminal. The second he explains as such:

The odd ones are the rcParams call, which makes the inline graphs bigger than the tiny Jupyter default, and the functools import, which will help us create ERAs over small portions of the season.

I’m not using Jupyter I’m using Rodeo as my IDE but I kept them all the same:

%matplotlib inline
plt.rcParams['figure.figsize'] = (12,9)

In the next section I use BeautifulSoup to scrape the data I want from ESPN:

url = 'http://www.espn.com/mlb/player/gamelog/_/id/30145/jake-arrieta'
r = requests.get(url)
year = 2016

date_pitched = []
full_ip = []
part_ip = []
earned_runs = []

tables = BeautifulSoup(r.text, 'lxml').find_all('table', class_='tablehead mod-player-stats')
for table in tables:
    for row in table.find_all('tr'): # Remove header
        columns = row.find_all('td')
        try:
            if re.match('[a-zA-Z]{3}\s', columns[0].text) is not None:
                date_pitched.append(
                    date(
                    year
                    , strptime(columns[0].text.split(' ')[0], '%b').tm_mon
                    , int(columns[0].text.split(' ')[1])
                    )
                )
                full_ip.append(str(columns[3].text).split('.')[0])
                part_ip.append(str(columns[3].text).split('.')[1])
                earned_runs.append(columns[6].text)
        except Exception as e:
            pass

This is basically a rehash of what I did for my Passer scraping (here, here, and here).

This proved a useful starting point, but unlike the NFL data on ESPN which has pre- and regular season breaks, the MLB data on ESPN has monthly breaks, like this:

Regular Season Games through October 2, 2016
DATE
Oct 1
Monthly Totals
DATE
Sep 24
Sep 19
Sep 14
Sep 9
Monthly Totals
DATE
Jun 26
Jun 20
Jun 15
Jun 10
Jun 4
Monthly Totals
DATE
May 29
May 23
May 17
May 12
May 7
May 1
Monthly Totals
DATE
Apr 26
Apr 21
Apr 15
Apr 9
Apr 4
Monthly Totals

However, all I wanted was the lines that correspond to columns[0].text with actual dates like ‘Apr 21’.

In reviewing how the dates were being displayed it was basically ‘%b %D’, i.e. May 12, Jun 4, etc. This is great because it means I want 3 letters and then a space and nothing else. Turns out, Regular Expressions are great for stuff like this!

After a bit of Googling I got what I was looking for:

re.match('[a-zA-Z]{3}\s', columns[0].text)

To get my regular expression and then just add an if in front and call it good!

The only issue was that as I ran it in testing, I kept getting no return data. What I didn’t realize is that returns a NoneType when it’s false. Enter more Googling and I see that in order for the if to work I have to add the is not None which leads to results that I wanted:

Oct 22
Oct 16
Oct 13
Oct 11
Oct 7
Oct 1
Sep 24
Sep 19
Sep 14
Sep 9
Jun 26
Jun 20
Jun 15
Jun 10
Jun 4
May 29
May 23
May 17
May 12
May 7
May 1
Apr 26
Apr 21
Apr 15
Apr 9
Apr 4

The next part of the transformation is to convert to a date so I can sort on it (and display it properly) later.

With all of the data I need, I put the columns into a Dictionary:

dic = {'date': date_pitched, 'Full_IP': full_ip, 'Partial_IP': part_ip, 'ER': earned_runs}

and then into a DataFrame:

games = pd.DataFrame(dic)

and apply some manipulations to the DataFrame:

games = games.sort_values(['date'], ascending=[True])
games[['Full_IP','Partial_IP', 'ER']] = games[['Full_IP','Partial_IP', 'ER']].apply(pd.to_numeric)

Now to apply some Baseball math to get the Earned Run Average:

games['IP'] = games.Full_IP + games.Partial_IP/3
games['GERA'] = games.ER/games.IP*9
games['CIP'] = games.IP.cumsum()
games['CER'] = games.ER.cumsum()
games['ERA'] = games.CER/games.CIP*9

In the next part of Dr. Drang’s post he writes a custom function to help create moving averages. It looks like this:

def rera(games, row):
    if row.name+1 < games:
        ip = df.IP[:row.name+1].sum()
        er = df.ER[:row.name+1].sum()
    else:
        ip = df.IP[row.name+1-games:row.name+1].sum()
        er = df.ER[row.name+1-games:row.name+1].sum()
    return er/ip*9

The only problem with it is I called my DataFrame games, not df. Simple enough, I’ll just replace df with games and call it a day, right? Nope:

def rera(games, row):
    if row.name+1 < games:
        ip = games.IP[:row.name+1].sum()
        er = games.ER[:row.name+1].sum()
    else:
        ip = games.IP[row.name+1-games:row.name+1].sum()
        er = games.ER[row.name+1-games:row.name+1].sum()
    return er/ip*9

When I try to run the code I get errors. Lots of them. This is because while i made sure to update the DataFrame name to be correct I overlooked that the function was using a parameter called games and Python got a bit confused about what was what.

OK, round two, replace the paramater games with games_t:

def rera(games_t, row):
    if row.name+1 < games_t:
        ip = games.IP[:row.name+1].sum()
        er = games.ER[:row.name+1].sum()
    else:
        ip = games.IP[row.name+1-games_t:row.name+1].sum()
        er = games.ER[row.name+1-games_t:row.name+1].sum()
    return er/ip*9

No more errors! Now we calculate the 3- and 4-game moving averages:

era4 = partial(rera, 4)
era3 = partial(rera,3)

and then add them to the DataFrame:

games['ERA4'] = games.apply(era4, axis=1)
games['ERA3'] = games.apply(era3, axis=1)

And print out a pretty graph:

plt.plot_date(games.date, games.ERA3, '-b', lw=2)
plt.plot_date(games.date, games.ERA4, '-r', lw=2)
plt.plot_date(games.date, games.GERA, '.k', ms=10)
plt.plot_date(games.date, games.ERA, '--k', lw=2)
plt.show()

Dr. Drang focused on Jake Arrieta (he is a Chicago guy after all), but I thought it was be interested to look at the Graphs for Arrieta and the top 5 finishers in the NL Cy Young Voting (because Clayton Kershaw was 5th place and I’m a Dodgers guy).

Here is the graph for Jake Arrieata:

And here are the graphs for the top 5 finishers in Ascending order in the 2016 NL Cy Young voting:

Max Scherzer winner of the 2016 NL Cy Young Award

Jon Lester

Kyle Hendricks

Madison Bumgarner

Clayton Kershaw:

I’ve not spent much time analyzing the data, but I’m sure that it says something. At the very least, it got me to wonder, ‘How many 0 ER games did each pitcher pitch?’

I also noticed that the stats include the playoffs (which I wasn’t intending). Another thing to look at later.

Legend:

  • Black Dot – ERA on Date of Game
  • Black Solid Line – Cummulative ERA
  • Blue Solid Line – 3-game trailing average ERA
  • Red Solid Line – 4-game trailing average ERA

Full code can be found on my Github Repo

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Web Scrapping – Passer Data (Part III)

In Part III I’m reviewing the code to populate a DataFrame with Passer data from the current NFL season.

To start I use the games DataFrame I created in Part II to create 4 new DataFrames:

  • reg_season_games – All of the Regular Season Games
  • pre_season_games – All of the Pre Season Games
  • gameshome – The Home Games
  • gamesaway – The Away Games

A cool aspect of the DataFrames is that you can treat them kind of like temporary tables (at least, this is how I’m thinking about them as I am mostly a SQL programmer) and create other temporary tables based on criteria. In the code below I’m taking the nfl_start_date that I defined in Part II as a way to split the data frame into pre / and regular season DataFrame. I then take the regular season DataFrame and split that into home and away DataFrames. I do this so I don’t double count the statistics for the passers.

#Start Section 3

reg_season_games = games.loc[games['match_date'] >= nfl_start_date]
pre_season_games = games.loc[games['match_date'] < nfl_start_date]

gameshome = reg_season_games.loc[reg_season_games['ha_ind'] == 'vs']
gamesaway = reg_season_games.loc[reg_season_games['ha_ind'] == '@']

Next, I set up some variables to be used later:

BASE_URL = 'http://www.espn.com/nfl/boxscore/_/gameId/{0}'

#Create the lists to hold the values for the games for the passers
player_pass_name = []
player_pass_catch = []
player_pass_attempt = []
player_pass_yds = []
player_pass_avg = []
player_pass_td = []
player_pass_int = []
player_pass_sacks = []
player_pass_sacks_yds_lost = []
player_pass_rtg = []
player_pass_week_id = []
player_pass_result = []
player_pass_team = []
player_pass_ha_ind = []
player_match_id = []
player_id = [] #declare the player_id as a list so it doesn't get set to a str by the loop below

headers_pass = ['match_id', 'id', 'Name', 'CATCHES','ATTEMPTS', 'YDS', 'AVG', 'TD', 'INT', 'SACKS', 'YRDLSTSACKS', 'RTG']

Now it’s time to start populating some of the list variables I created above. I am taking the week_id, result, team_x, and ha_ind columns from the games DataFrame (I’m sure there is a better way to do this, and I will need to revisit it in the future)

player_pass_week_id.append(gamesaway.week_id)
player_pass_result.append(gamesaway.result)
player_pass_team.append(gamesaway.team_x)
player_pass_ha_ind.append(gamesaway.ha_ind)

Now for the looping (everybody’s favorite part!). Using BeautifulSoup I get the div of class col column-one gamepackage-away-wrap. Once I have that I get the table rows and then loop through the data in the row to get what I need from the table holding the passer data. Some intersting things happening below:

  • The Catches / Attempts and Sacks / Yrds Lost are displayed as a single column each (even though each column holds 2 statistics). In order to fix this I use the index() method and get all of the data to the left of a character (- and / respectively for each column previously mentioned) and append the resulting 2 items per column (so four in total) to 2 different lists (four in total).

The last line of code gets the ESPN player_id, just in case I need/want to use it later. 

for index, row in gamesaway.iterrows():
    print(index)
    try:
        request = requests.get(BASE_URL.format(index))
        table_pass = BeautifulSoup(request.text, 'lxml').find_all('div', class_='col column-one gamepackage-away-wrap')

        pass_ = table_pass[0]
        player_pass_all = pass_.find_all('tr')


        for tr in player_pass_all:
            for td in tr.find_all('td', class_='sacks'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_sacks.append(int(td.text[0:td.text.index('-')]))
                        player_pass_sacks_yds_lost.append(int(td.text[td.text.index('-')+1:]))
            for td in tr.find_all('td', class_='c-att'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_catch.append(int(td.text[0:td.text.index('/')]))
                        player_pass_attempt.append(int(td.text[td.text.index('/')+1:]))
            for td in tr.find_all('td', class_='name'):
                for t in tr.find_all('td', class_='name'):
                    for s in t.find_all('span', class_=''):
                        if t.text != 'TEAM':
                            player_pass_name.append(s.text)
            for td in tr.find_all('td', class_='yds'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_yds.append(int(td.text))
            for td in tr.find_all('td', class_='avg'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_avg.append(float(td.text))
            for td in tr.find_all('td', class_='td'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_td.append(int(td.text))
            for td in tr.find_all('td', class_='int'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_int.append(int(td.text))
            for td in tr.find_all('td', class_='rtg'):
                for t in tr.find_all('td', class_='name'):
                    if t.text != 'TEAM':
                        player_pass_rtg.append(float(td.text))
                        player_match_id.append(index)
            #The code below cycles through the passers and gets their ESPN Player ID
            for a in tr.find_all('a', href=True):
                player_id.append(a['href'].replace("http://www.espn.com/nfl/player/_/id/","")[0:a['href'].replace("http://www.espn.com/nfl/player/_/id/","").index('/')])

    except Exception as e:
        pass

With all of the data from above we now populate our DataFrame using specific headers (that’s why we set the headers_pass variable above):

player_passer_data = pd.DataFrame(np.column_stack((
player_match_id,
player_id,
player_pass_name,
player_pass_catch,
player_pass_attempt,
player_pass_yds,
player_pass_avg,
player_pass_td,
player_pass_int,
player_pass_sacks,
player_pass_sacks_yds_lost,
player_pass_rtg
)), columns=headers_pass)

An issue that I ran into as I was playing with the generated DataFrame was that even though I had set the numbers generated in the for loop above to be of type int anytime I would do something like a sum() on the DataFrame the numbers would be concatenated as though they were strings (because they were!).

After much Googling I came across a useful answer on StackExchange (where else would I find it, right?)

What it does is to set the data type of the columns from string to int

player_passer_data[['TD', 'CATCHES', 'ATTEMPTS', 'YDS', 'INT', 'SACKS', 'YRDLSTSACKS','AVG','RTG']] = player_passer_data[['TD', 'CATCHES', 'ATTEMPTS', 'YDS', 'INT', 'SACKS', 'YRDLSTSACKS','AVG','RTG']].apply(pd.to_numeric)

OK, so I’ve got a DataFrame with passer data, I’ve got a DataFrame with away game data, now I need to join them. As expected, pandas has a way to join DataFrame data … with the join method obviously!

I create a new DataFrame called game_passer_data which joins player_passer_data with games_away on their common key match_id. I then have to use set_index to make sure that the index stays set to match_id … If I don’t then the index is reset to an auto-incremented integer. 

game_passer_data = player_passer_data.join(gamesaway, on='match_id').set_index('match_id')

This is great, but now game_passer_data has all of these extra columns. Below is the result of running game_passer_data.head() from the terminal:

id          Name  CATCHES  ATTEMPTS  YDS  AVG  TD  INT  SACKS  \
match_id
400874518  2577417  Dak Prescott       22        30  292  9.7   0    0      4
400874674  2577417  Dak Prescott       23        32  245  7.7   2    0      2
400874733  2577417  Dak Prescott       18        27  247  9.1   3    1      2
400874599  2577417  Dak Prescott       21        27  247  9.1   3    0      0
400874599    12482  Mark Sanchez        1         1    8  8.0   0    0      0

           YRDLSTSACKS                        ...                          \
match_id                                      ...
400874518           14                        ...
400874674           11                        ...
400874733           14                        ...
400874599            0                        ...
400874599            0                        ...

           ha_ind  match_date                  opp result          team_x  \
match_id
400874518       @  2016-09-18  washington-redskins      W  Dallas Cowboys
400874674       @  2016-10-02  san-francisco-49ers      W  Dallas Cowboys
400874733       @  2016-10-16    green-bay-packers      W  Dallas Cowboys
400874599       @  2016-11-06     cleveland-browns      W  Dallas Cowboys
400874599       @  2016-11-06     cleveland-browns      W  Dallas Cowboys

          week_id prefix_1             prefix_2               team_y  \
match_id
400874518       2      wsh  washington-redskins  Washington Redskins
400874674       4       sf  san-francisco-49ers  San Francisco 49ers
400874733       6       gb    green-bay-packers    Green Bay Packers
400874599       9      cle     cleveland-browns     Cleveland Browns
400874599       9      cle     cleveland-browns     Cleveland Browns

                                                         url
match_id
400874518  http://www.espn.com/nfl/team/_/name/wsh/washin...
400874674  http://www.espn.com/nfl/team/_/name/sf/san-fra...
400874733  http://www.espn.com/nfl/team/_/name/gb/green-b...
400874599  http://www.espn.com/nfl/team/_/name/cle/clevel...
400874599  http://www.espn.com/nfl/team/_/name/cle/clevel...

That is nice, but not exactly what I want. In order to remove the extra columns I use the drop method which takes 2 arguments:

  • what object to drop
  • an axis which determine what types of object to drop (0 = rows, 1 = columns):

Below, the object I define is a list of columns (figured that part all out on my own as the documentation didn’t explicitly state I could use a list, but I figured, what’s the worst that could happen?)

game_passer_data = game_passer_data.drop(['opp', 'prefix_1', 'prefix_2', 'url'], 1)

Which gives me this:

id          Name  CATCHES  ATTEMPTS  YDS  AVG  TD  INT  SACKS  \
match_id
400874518  2577417  Dak Prescott       22        30  292  9.7   0    0      4
400874674  2577417  Dak Prescott       23        32  245  7.7   2    0      2
400874733  2577417  Dak Prescott       18        27  247  9.1   3    1      2
400874599  2577417  Dak Prescott       21        27  247  9.1   3    0      0
400874599    12482  Mark Sanchez        1         1    8  8.0   0    0      0

           YRDLSTSACKS    RTG ha_ind  match_date result          team_x  \
match_id
400874518           14  103.8      @  2016-09-18      W  Dallas Cowboys
400874674           11  114.7      @  2016-10-02      W  Dallas Cowboys
400874733           14  117.4      @  2016-10-16      W  Dallas Cowboys
400874599            0  141.8      @  2016-11-06      W  Dallas Cowboys
400874599            0  100.0      @  2016-11-06      W  Dallas Cowboys

          week_id               team_y
match_id
400874518       2  Washington Redskins
400874674       4  San Francisco 49ers
400874733       6    Green Bay Packers
400874599       9     Cleveland Browns
400874599       9     Cleveland Browns

I finally have a DataFrame with the data I care about, BUT all of the column names are wonky!

This is easy enough to fix (and should have probably been fixed earlier with some of the objects I created only containing the necessary columns, but I can fix that later). By simply renaming the columns as below:

game_passer_data.columns = ['id', 'Name', 'Catches', 'Attempts', 'YDS', 'Avg', 'TD', 'INT', 'Sacks', 'Yards_Lost_Sacks', 'Rating', 'HA_Ind', 'game_date', 'Result', 'Team', 'Week', 'Opponent']

I now get the data I want, with column names to match!

id          Name  Catches  Attempts  YDS  Avg  TD  INT  Sacks  \
match_id
400874518  2577417  Dak Prescott       22        30  292  9.7   0    0      4
400874674  2577417  Dak Prescott       23        32  245  7.7   2    0      2
400874733  2577417  Dak Prescott       18        27  247  9.1   3    1      2
400874599  2577417  Dak Prescott       21        27  247  9.1   3    0      0
400874599    12482  Mark Sanchez        1         1    8  8.0   0    0      0

           Yards_Lost_Sacks  Rating HA_Ind   game_date Result            Team  \
match_id
400874518                14   103.8      @  2016-09-18      W  Dallas Cowboys
400874674                11   114.7      @  2016-10-02      W  Dallas Cowboys
400874733                14   117.4      @  2016-10-16      W  Dallas Cowboys
400874599                 0   141.8      @  2016-11-06      W  Dallas Cowboys
400874599                 0   100.0      @  2016-11-06      W  Dallas Cowboys

          Week             Opponent
match_id
400874518    2  Washington Redskins
400874674    4  San Francisco 49ers
400874733    6    Green Bay Packers
400874599    9     Cleveland Browns
400874599    9     Cleveland Browns

I’ve posted the code for all three parts to my GitHub Repo.

Work that I still need to do:

  1. Add code to get the home game data
  2. Add code to get data for the other position players
  3. Add code to get data for the defense

When I started this project on Wednesday I had only a bit of exposure to very basic aspects of Python and my background as a developer. I’m still a long way from considering myself proficient in Python but I know more now that I did 3 days ago and for that I’m pretty excited! It’s also given my an excuse reason to write some stuff which is a nice side effect.

Posted on

Web Scrapping – Passer Data (Part II)

On a previous post I went through my new found love of Fantasy Football and the rationale behind the ‘why’ of this particular project. This included getting the team names and their URLs from the ESPN website.

As before, let’s set up some basic infrastructure to be used later:

from time import strptime

year = 2016 # allows us to change the year that we are interested in.
nfl_start_date = date(2016, 9, 8)
BASE_URL = 'http://espn.go.com/nfl/team/schedule/_/name/{0}/year/{1}/{2}' #URL that we'll use to cycle through to get the gameid's (called match_id)

match_id = []
week_id = []
week_date = []
match_result = []
ha_ind = []
team_list = []

Next, we iterate through the teams dictionary that I created yesterday:

for index, row in teams.iterrows():
    _team, url = row['team'], row['url']
    r=requests.get(BASE_URL.format(row['prefix_1'], year, row['prefix_2']))
    table = BeautifulSoup(r.text, 'lxml').table
    for row in table.find_all('tr')[2:]: # Remove header
        columns = row.find_all('td')
        try:
            for result in columns[3].find('li'):
                match_result.append(result.text)
                week_id.append(columns[0].text) #get the week_id for the games dictionary so I know what week everything happened
                _date = date(
                    year,
                    int(strptime(columns[1].text.split(' ')[1], '%b').tm_mon),
                    int(columns[1].text.split(' ')[2])
                )
                week_date.append(_date)
                team_list.append(_team)
                for ha in columns[2].find_all('li', class_="game-status"):
                    ha_ind.append(ha.text)
            for link in columns[3].find_all('a'): # I realized here that I didn't need to do the fancy thing from the site I was mimicking http://danielfrg.com/blog/2013/04/01/nba-scraping-data/
                match_id.append(link.get('href')[-9:])

        except Exception as e:
            pass

Again, we set up some variables to be used in the for loop. But I want to really talk about the try portion of my code and the part where the week_date is being calculated.

Although I’ve been developing and managing developers for a while, I’ve not had the need to use a construct like try. (I know, right, weird!)

The basic premise of the try is that it will execute some code and if it succeeds that code will be executed. If not, it will go to the exception portion. For Python (and maybe other languages, I’m not sure) the exception MUST have something in it. In this case, I use Python’s pass function, which basically says, ‘hey, just forget about doing anything’. I’m not raising any errors here because I don’t care if the result is ‘bad’ I just want to ignore it because there isn’t any data I can use.

The other interesting (or gigantic pain in the a$$) thing is that the way ESPN displays dates on the schedule page is as Day of Week, Month Day, i.e. Sun Sep 11. There is no year. I think this is because for the most part the regular season for an NFL is always in the same calendar year. However, this year the last game of the season, in week 17, is in January. Since I’m only getting games that have been played, I’m safe for a couple more weeks, but this will need to be addressed, otherwise the date of the last games of the 2016 season will show as January 2016, instead of January 2017.

Anyway, I digress. In order to change the displayed date to a date I can actually use is I had to get the necessary function. In order to get that I had to add the following line to my code from yesterday

from time import strptime

This allows me to make some changes to the date (see where _date is being calculated in for result in columns[3].find('li'): portion of the try:.

One of the things that confused the heck out of me initially was the way the date is being stored in the list week_date. It is in the form datetime.date(2016, 9, 1), but I was expecting it to be stored as 2016-09-01. I did a couple of things to try and fix this, especially because once the list was added to the gamesdic dictionary and then used in the games DataFrame the week_date was then stored as 1472688000000 which is the milliseconds since Jan 1, 1970 to the date of the game, but it took an embarising amount of Googling to realize discover this.

With this new discovery, I forged on. The last two things that I needed to do was to create a dictionary to hold my data with all of my columns:

gamesdic = {'match_id': match_id, 'week_id': week_id, 'result': match_result, 'ha_ind': ha_ind, 'team': team_list, 'match_date': week_date}

With dictionary in hand I was able to create a DataFrame:

games = pd.DataFrame(gamesdic).set_index('match_id')

The line above is frighteningly simple. It’s basically saying, hey, take all of the data from the gamesdic dictionary and make the match_id the index.

To get the first part, see my post Web Scrapping – Passer Data (Part I).

Posted on

Web Scrapping – Passer Data (Part I)

For the first time in many years I’ve joined a Fantasy Football league with some of my family. One of the reasons I have not engaged in the Fantasy football is that, frankly, I’m not very good. In fact, I’m pretty bad. I have a passing interest in Football, but my interests lie more with Baseball than football (especially in light of the NFLs policy on punishing players for some infractions of league rules, but not punishing them for infractions of societal norms (see Tom Brady and Ray Lewis respectively).

That being said, I am in a Fantasy Football league this year, and as of this writing am a respectable 5-5 and only 2 games back from making the playoffs with 3 games left.

This means that what I started on yesterday I really should have started on much sooner, but I didn’t.

I had been counting on ESPN’s ‘projected points’ to help guide me to victory … it’s working about as well as flipping a coin (see my record above).

I had a couple of days off from work this week and some time to tinker with Python, so I thought, what the hell, let’s see what I can do.

Just to see what other people had done I did a quick Google Search and found someone that had done what I was trying to do with data from the NBA in 2013.

Using their post as a model I set to work.

The basic strategy I am mimicking is to:

I start of importing some standard libraries pandas, requests, and BeautifulSoup (the other libraries are for later).

import pandas as pd
import requests
from bs4 import BeautifulSoup
import csv
import numpy as np
from datetime import datetime, date

Next, I need to set up some variables. BeautifulSoup is a Python library for pulling data out of HTML and XML files.. It’s pretty sweet. The code below is declaring a URL to scrape and then users the requests library to get the actual HTML of the page and put it into a variable called r. 

url = 'http://espn.go.com/nfl/teams'
r = requests.get(url)

r has a method called text which I’ll use with BeautifulSoup to create the soup. The 'lxml' declares the parser type to be used. The default is lxml and when I left it off I was presented with a warning, so I decided to explicitly state which parser I was going to be using to avoid the warning. 

soup = BeautifulSoup(r.text, 'lxml')

Next I use the find_all function from BeautifulSoup. The cool thing about find_all is that you can either pass just a tag element, i.e. li or p, but you can add an additional class_ argument (notice the underscore at the end … I missed it more than once and got an error because class is a keyword used by Python). Below I’m getting all of the `ul’ elements of the class type ‘medium-logos’.

tables = soup.find_all('ul', class_='medium-logos')

Now I set up some list variables to hold the items I’ll need for later use to create my dictionary

teams = []
prefix_1 = []
prefix_2 = []
teams_urls = []

Now, we do some actual programming:

Using a nested for loop to find all of the li elements in the variable called lis which is based on the variable tables (recall this is all of the HTML from the page I scrapped that has only the tags that match <ul class='medium-logos></ul> and all of the content between them).

The nested for loop creates 2 new variables which are used to populate the 4 lists from above. The creating of the info variable gets the a tag from the li tags. The url variable takes the href tag from the info variable. In order to add an item to a list (remember, all of the lists above are empty at this point) we have to invoke the method append on each of the lists with the data that we care about (as we look through).

The function split can be used on a string (which url is). It allows you to take a string apart based on a passed through value and convert the output into a list. This is super useful with URLs since there are many cases where we’re trying to get to the path. Using split('/') allows the URL to be broken into it’s constituent parts. The negative indexes used allows you to go from right to left instead of left to right.

To really break this down a bit, if we looked at just one of the URLs we’d get this:

http://www.espn.com/nfl/team/_/name/ten/tennessee-titans

The split('/') command will turn the URL into this:

['http:', '', 'www.espn.com', 'nfl', 'team', '_', 'name', 'ten', 'tennessee-titans']

Using the negative index allows us to get the right most 2 values that we need. 

for table in tables:
    lis = table.find_all('li')
    for li in lis:
        info = li.h5.a
        teams.append(info.text)
        url = info['href']
        teams_urls.append(url)
        prefix_1.append(url.split('/')[-2])
        prefix_2.append(url.split('/')[-1])

Now we put it all together into a dictionary

dic = {'url': teams_urls, 'prefix_2': prefix_2, 'prefix_1': prefix_1, 'team': teams}
teams = pd.DataFrame(dic)

This is the end of part 1. Parts 2 and 3 will be coming later this week.

I’ve also posted all of the code to my GitHub Repo.

Posted on

Pushing Changes from Pythonista to GitHub – Step 1

With the most recent release of the iOS app Workflow I was toying with the idea of writing a workflow that would allow me to update / add a file to a GitHub repo via a workflow.

My thinking was that since Pythonista is only running local files on my iPad if I could use a workflow to access the api elements to push the changes to my repo that would be pretty sweet.

In order to get this to work I’d need to be able to accomplosh the following things (not necessarily in this order)

  • Have the workflow get a list of all of the repositories in my GitHub
  • Get the current contents of the app to the clip board
  • Commit the changes to the master of the repo

I have been able to write a Workflow that will get all of the public repos of a specified github user. Pretty straight forward stuff.

The next thing I’m working on getting is to be able to commit the changes from the clip board to a specific file in the repo (if one is specified) otherwise a new file would be created.

I really just want to ‘have the answer’ for this, but I know that the journey will be the best part of getting this project completed.

So for now, I continue to read the GitHub API Documentation to discover exactly how to do what I want to do.