AayushSameerShah

from wordcloud import WordCloud, STOPWORDS

text = ' '.join(df_movie['listed_in'])

plt.rcParams['figure.figsize'] = (12,12)
wordcloud = WordCloud(background_color = 'black',colormap='vlag', width = 1200,  height = 1200, max_words = 121).generate(text)
plt.imshow(wordcloud)
plt.axis('off')
plt.show()

AayushSameerShah

from matplotlib import cm
color = cm.inferno_r(np.linspace(.4, .8, 18))

plt.plot(color= color)

AayushSameerShah

x = ["A", "B", "C", "D"]
y = [1, 2, 3, 4]
plt.barh(x, y)

for index, value in enumerate(y):
    plt.text(value, index, str(value))

AayushSameerShah

rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Tahoma']

COLOR = 'blue'
mpl.rcParams['text.color'] = COLOR
mpl.rcParams['axes.labelcolor'] = COLOR
mpl.rcParams['xtick.color'] = COLOR
mpl.rcParams['ytick.color'] = COLOR

AayushSameerShah

# SIMPLE chhe, Don't worry...
def func(x):
    return x ** 2

x = np.linspace(0, 10)
y = func(x)

fig, ax = plt.subplots()
ax.plot(x, y, 'r', linewidth=2)
ax.set_ylim(bottom=0)

AayushSameerShah

# Create a unique set, so it becomes clear 
genres = set()
for gen in df.Genre:
    for single_gen in map(str.strip, gen.split(",")):
        genres.add(single_gen)
        
# Create dict to store ids of that category
genre_ids = dict()
for gen in genres:
    genre_ids[gen] = []

AayushSameerShah

for i in range(50):
    print("Fizz" * (i % 3 == 0) + "Buzz" * (i % 5 == 0) or False)

AayushSameerShah

# 1. Use of .agg in groupy
DF.groupby('').agg(['mean', 'max'])
                        # or
DF.groupby('').agg({'col1:': 'max',
                   'col2': 'min'})


# 2. Amazing, when using agg from above or other, if 
#    you want to rename the columns ON THE AIR, then...
DF.groupby('').agg(

AayushSameerShah

# 1. Describe... some more
DF.describe(include= ['dtype'])


# 2. Use the freaking .str.contains()
DF.col.str.contains()


# 3. Format for a while! (COOLEST)
pd.set_option('display.float_format', '{:,}'.format)

AayushSameerShah

'''
State     District      Population(values)

Guj       Kutch         30
          Ahmedabad     100
          Surat         50
          Vadodra       20
          Jak           10
          
Maha      Mumbai        130