fantasy premier league api python

The FPLPandas class in this package uses the excellent FPL library to retrieve data from the Fantasy Premier League API.It maps the results to the pandas data frames so that the data can be analysed interactively, e.g. It’s not all about value of course. There’s a lot of value available in the goalkeeper position. Next, we’ll create a pivot_table on the column position , and look at value-by-position: After creating the pivot_table and assigning the pivot_table to the variable pivot , we’ll sort the pivot table descending by value: Interesting. join our Discord server or send an email to For the code documentation, please visit the Documentation Github Pages. I placed this script at the bottom of the HTML. We could use the function from earlier, but to avoid having to download the JSON file and save it to disk we can use a much shorter piece of code: Hmm, not quite what we were after; the values we want are all inside the ‘results’ column. way around. Introductions and Humble Brags. This part of the documentation is mostly an introduction on how to use fpl There is no documentation and the API isn’t publicly listed anywhere as far as I can tell, which makes me think that it’s not primarily intended for public use. Download the file for your platform. The final goal of our project was to write a Python Algorithm, which uses the data from our analysis to make “smart” picks and build the most optimal Fantasy League squad given our limited budget of 100MM. Yep, only 31. or .zip file. Site map. I’ve found there is a void in my life as of late, and I still find myself looking at the /r/fantasypl subreddit regularly even though I know we’re months away from the next season starting. Head over to your Anaconda Navigator and make sure to add the package needed to whatever environment you activate for your Jupyter Notebook work. Pandas wrapper for Fantasy Premier League API.! Okay, so, I know mostly php, html, and I know a bit about APIs. Next, let’s add the team name. Take a look, elements_df = pd.DataFrame(json['elements']), slim_elements_df = elements_df[['second_name','team','element_type','selected_by_percent','now_cost','minutes','transfers_in','value_season','total_points']], slim_elements_df['position'] ='id').singular_name), slim_elements_df['team'] ='id').name), slim_elements_df['value'] = slim_elements_df.value_season.astype(float), slim_elements_df.sort_values('value',ascending=False).head(10), slim_elements_df.pivot_table(index='position',values='value',aggfunc=np.mean).reset_index(), pivot=slim_elements_df.pivot_table(index='position',values='value',aggfunc=np.mean).reset_index(), pivot.sort_values('value',ascending=False), slim_elements_df = slim_elements_df.loc[slim_elements_df.value > 0], pivot = slim_elements_df.pivot_table(index='position',values='value',aggfunc=np.mean).reset_index(), team_pivot = slim_elements_df.pivot_table(index='team',values='value',aggfunc=np.mean).reset_index(), team_pivot.sort_values('value',ascending=False), fwd_df = slim_elements_df.loc[slim_elements_df.position == 'Forward'], mid_df = slim_elements_df.loc[slim_elements_df.position == 'Midfielder'], def_df = slim_elements_df.loc[slim_elements_df.position == 'Defender'], goal_df = slim_elements_df.loc[slim_elements_df.position == 'Goalkeeper'], goal_df.sort_values('value',ascending=False).head(10), def_df.sort_values('value',ascending=False).head(10), slim_elements_df.to_csv('~/Desktop/fpl_data.csv'), Build a DataFrame from your API request response, Export to CSV for Analysis in Google Sheets or Excel (if that’s your thing),', Go Programming Language for Artificial Intelligence and Data Science of the 20s, Tiny Machine Learning: The Next AI Revolution.

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