erraticgenerator

import matplotlib.pyplot as plt
from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
import numpy as np

erraticgenerator

# option 1. dynamically download JSON
# url = 'https://www.googleapis.com/webfonts/v1/webfonts?key='
# key = 'YOUR-API-KEY'
# data = pd.read_json(url+key, orient='')

# option 2. use JSON already downloaded (replace with your own file path)
df = pd.read_json('../../input/fonts-master.json')
# df.head()

# flatten the JSON hierarchy (easier to handle this way)

erraticgenerator

# select only the columns we need
cols = ['family', 'variants', 'subsets', 'category']
df = df[cols]
# df.head(5)

# Remove any space from family string so that it matchs with file name convention.
df.family = [name.replace(' ', '') for name in df.family]
df.head(5)

erraticgenerator

mlb = MultiLabelBinarizer()

# one-hot encoding + prefix
df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('variants')),
                          columns=[x for x in mlb.classes_],
                          index=df.index))
df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('subsets')),
                          columns=['subsets_' + x for x in mlb.classes_],
                          index=df.index))
df = df.join(pd.get_dummies(df['category'], prefix="category")).drop(['category'], axis=1)

erraticgenerator

col_names = {
    "100": "thin",
    "100italic": "thinitalic",
    "200": "extralight",
    "200italic": "extralightitalic",
    "300": "light",
    "300italic": "lightitalic",
    "400": "regular",
    "regular": "regular",
    "400italic": "italic",

erraticgenerator

# modules we need this time
import matplotlib.pyplot as plt
from PIL import Image,ImageDraw,ImageFont
import pandas as pd
import numpy as np

# load the CSV we created in the last post
df = pd.read_csv('./google-fonts-annotaion.csv')

erraticgenerator

print('Number of fonts in each variant:')
print(df[df.filter(regex=r'^variants_', axis=1).columns].eq(1).sum().sort_values(ascending=False))

print('\nNumber of fonts in each subset:')
print(df[df.filter(regex=r'^subsets_', axis=1).columns].eq(1).sum().sort_values(ascending=False))

print('\nNumber of fonts in each category:')
print(df[df.filter(regex=r'^category_', axis=1).columns].eq(1).sum().sort_values(ascending=False))

erraticgenerator

mask = (df.filter(regex='thin', axis=1).sum(axis=1).astype(bool) &
        df.filter(regex='regular', axis=1).sum(axis=1).astype(bool) & 
        df.filter(regex='thai', axis=1).sum(axis=1).astype(bool))
df_selected = df[mask]
df_selected

erraticgenerator

mask = (df.filter(regex='chinese', axis=1).sum(axis=1).astype(bool) & 
       df.filter(regex='sans-serif', axis=1).sum(axis=1).astype(bool))
df_selected = df[mask]
df_selected

erraticgenerator

regex_filters = ['_regular', '_japanese', '_serif']

df_new = pd.concat([df.filter(regex=regex, axis=1).sum(axis=1).astype(bool) for regex in regex_filters], axis=1)
mask = df_new.all(axis=1)

df.loc[mask]