Setup

In [2]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

from numpy.random import normal, exponential, randn, rand, random_sample
In [3]:
# Custom color palette
from cycler import cycler
colors = ["#e69f00", "#56b4e9", "#009e73", "#d55e00", "#cc799c"]
plt.rc("axes", prop_cycle=cycler("color", colors))

1D data

Histograms

In [3]:
x = randn(1000)
bins = np.linspace(-10, 10)
plt.hist(x, bins=bins, density=True)
plt.show()
In [4]:
sns.distplot(x)
plt.savefig("figs/sns_distplot.png")
plt.show()
In [5]:
sns.kdeplot(x, cumulative=True)
plt.xlim(-10, 10)
plt.show()

2D data

Line plotting

In [6]:
x = np.array([1, 2, 3, 4])
y = x**2
plt.plot(x, y)
plt.show()

df = pd.DataFrame({"x": x, "y":y})
df.plot("x", "y")
plt.show()

Filling areas

In [7]:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

np.random.seed(33)
time = pd.date_range('2000-1-1', periods=150, freq='B')
price = pd.Series(100+randn(150).cumsum(), index=time)
avg = price.rolling(20).mean()
std = price.rolling(20).std()

plt.plot(price.index, price, 'k')
plt.plot(avg.index, avg, 'b')
plt.fill_between(std.index, avg-2*std, avg+2*std, color='b', alpha=0.2)
plt.ylabel("Price")
plt.savefig("figs/plt_moving_average.png")
plt.show()

Errorbars

In [8]:
x = np.arange(0, 2*np.pi, 0.1)
yerr = 0.3
noise = yerr * np.random.randn(*x.shape)
y = np.sin(x) + noise
plt.errorbar(x, y, yerr=yerr, fmt="o")
plt.savefig("figs/plt_errorbar.png")
plt.show()

Scatter plots

In [9]:
x = randn(1000)
y = exponential(1, 1000)
z = 15 - exponential(1, 1000)

plt.scatter(x, y, label="y")
plt.scatter(x, z, label="z")
plt.legend()
plt.savefig("figs/plt_scatter.png")

2D histogram

In [10]:
x = randn(1000)
y = exponential(size=1000)
hist = plt.hist2d(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.show()

Hexagonal histogram

In [25]:
x, y = randn(2, 10000)
sns.jointplot(x, y, kind="hex", color="k")

Images

In [11]:
path = "figs/python.png"
img = plt.imread(path)
fig1 = plt.imshow(img)
plt.savefig("figs/plt_imshow.png")
plt.show()

data = rand(*img.shape)
data[img > 0.95] = 1
fig = plt.imshow(data)
plt.show()

Violin plots

In [12]:
mus = 0, 1.5, 2.2
data = [normal(mu, 1, 1000) for mu in mus]
plt.violinplot(data, positions=mus)
plt.xlabel(r"$\mu$")
plt.show()

Split violin plots

In [219]:
tips = sns.load_dataset("tips")
tips["percent"] = tips.tip / tips.total_bill
sns.violinplot("day", "percent", "sex", data=tips, split=True, palette=["#e69f00", "#56b4e9"], saturation=1, linewidth=1)
plt.savefig("figs/sns_violin.png")
plt.show()

Bar charts

In [226]:
bottom = 0
for sex, df in tips.groupby("sex", sort=False):
    df = df.groupby("day")["tip"].mean().reset_index()
    plt.bar(df["day"], df["tip"], 0.8, bottom, label=sex)
    bottom = df["tip"]
plt.legend()
plt.savefig("figs/plt_bar.png")

Geospatial plotting

Folium

In [56]:
import folium
m = folium.Map(location=[47.3686, 8.5391])
m
Out[56]:
In [16]:
import pandas as pd


url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
state_geo = f'{url}/us-states.json'
state_unemployment = f'{url}/US_Unemployment_Oct2012.csv'
state_data = pd.read_csv(state_unemployment)

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=state_geo,
    name='choropleth',
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Unemployment Rate (%)'
).add_to(m)

folium.LayerControl().add_to(m)

m
Out[16]:

Geopandas

In [17]:
import geopandas
In [18]:
geopandas.datasets.available
Out[18]:
['naturalearth_cities', 'naturalearth_lowres', 'nybb']
In [19]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.head()
Out[19]:
pop_est continent name iso_a3 gdp_md_est geometry
0 920938 Oceania Fiji FJI 8374.0 (POLYGON ((180 -16.06713266364245, 180 -16.555...
1 53950935 Africa Tanzania TZA 150600.0 POLYGON ((33.90371119710453 -0.950000000000000...
2 603253 Africa W. Sahara ESH 906.5 POLYGON ((-8.665589565454809 27.65642588959236...
3 35623680 North America Canada CAN 1674000.0 (POLYGON ((-122.84 49.00000000000011, -122.974...
4 326625791 North America United States of America USA 18560000.0 (POLYGON ((-122.84 49.00000000000011, -120 49....
In [20]:
world.plot()
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f991a529438>
In [21]:
world.columns
Out[21]:
Index(['pop_est', 'continent', 'name', 'iso_a3', 'gdp_md_est', 'geometry'], dtype='object')
In [57]:
fig = plt.figure(figsize=(20, 5))
ax = fig.gca()
world = world[(world.pop_est>0) & (world.name!="Antarctica")]
world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
world.plot(column='gdp_per_cap', ax=ax, legend=True, cmap="OrRd")
plt.savefig("figs/geopandas_gdp_per_cap.png")
plt.show()
In [23]:
fig = plt.figure(figsize=(20, 5))
ax = fig.gca()

cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
world.plot(color='white', edgecolor='black', ax=ax)
cities.plot(ax=ax, marker='*', color='green', markersize=5)
Out[23]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f98d7a554e0>

3D data

Contour plots

In [24]:
import noise
pnoise2 = np.vectorize(noise.pnoise2)
In [25]:
x = np.arange(-3, 3, 0.1)
y = np.arange(-3, 3, 0.1)
X, Y = np.meshgrid(x, y)
z = pnoise2(X, Y)
plt.contour(X, Y, z)
plt.savefig("figs/plt_contour.png")
plt.show()
In [26]:
plt.contourf(X, Y, z, 20, cmap='RdGy')
plt.savefig("figs/plt_contourf.png")
plt.show()

3D visualization

In [27]:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

cmap = plt.cm.viridis
x_, y_, z_ = X.flatten(), Y.flatten(), z.flatten()
surf = ax.plot_trisurf(x_, y_, z_, cmap=cmap)
plt.colorbar(surf)
plt.savefig("figs/plt_trisurf.png")
plt.show()

Ternary plot

In [4]:
import ternary
fig, tax = ternary.figure(scale=100)
fig.set_size_inches(5, 5)
k, n = 100, 50
a = k * rand(n); b = (k - a) * rand(n)
c = k - a - b
tax.scatter(np.array([[a, b, c]]))
tax.scatter([[20, 35, 45]])
tax.right_corner_label("A")
tax.top_corner_label("B")
tax.left_corner_label("C")
tax.left_axis_label("c [%]")
tax.right_axis_label("b [%]")
tax.bottom_axis_label("a [%]")
tax.gridlines(multiple=20, color="gray")
tax.ticks(axis='lbr', multiple=20)
tax.boundary(linewidth=1)
tax.get_axes().axis('off')
tax.savefig("figs/ternary.png")
tax.show()

4D data

Scatterplot

In [28]:
planets = sns.load_dataset("planets")

cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
ax = sns.scatterplot(x="distance", y="orbital_period",
                     hue="year", size="mass",
                     palette=cmap, sizes=(10, 200),
                     data=planets)
plt.savefig("figs/sns_scatterplot.png")
plt.show()

ND data

Parallel coordinates

In [29]:
from pandas.plotting import parallel_coordinates

iris = pd.read_csv("data/iris.csv")
parallel_coordinates(iris, "Name")
plt.show()

Spider graph

In [22]:
from math import pi
 
# Set data
df = pd.DataFrame({
'group': ['A','B','C','D'],
'var1': [38, 1.5, 30, 4],
'var2': [29, 10, 9, 34],
'var3': [8, 39, 23, 24],
'var4': [7, 31, 33, 14],
'var5': [28, 15, 32, 14]
})
 
# ------- PART 1: Define a function that do a plot for one line of the dataset!
 
def make_spider(row, title, color):
 
    # number of variable
    categories = list(df)[1:]
    N = len(categories)

    # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # Initialise the spider plot
    ax = plt.subplot(2,2,row+1, polar=True, )

    # If you want the first axis to be on top:
    ax.set_theta_offset(pi / 2)
    ax.set_theta_direction(-1)

    # Draw one axe per variable + add labels labels yet
    plt.xticks(angles[:-1], categories, color='grey', size=8)

    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([10,20,30], ["10","20","30"], color="grey", size=7)
    plt.ylim(0,40)

    # Ind1
    values=df.loc[row].drop('group').values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, color=color, linewidth=2, linestyle='solid')
    ax.fill(angles, values, color=color, alpha=0.4)

    # Add a title
    plt.title(title, size=11, color=color, y=1.1)

# ------- PART 2: Apply to all individuals
# initialize the figure
my_dpi=96
plt.figure(figsize=(1000/my_dpi, 1000/my_dpi), dpi=my_dpi)

# Create a color palette:
my_palette = plt.cm.get_cmap("Set2", len(df.index))
 
# Loop to plot
for row in range(0, len(df.index)):
    make_spider( row=row, title='group '+df['group'][row], color=my_palette(row))
plt.savefig("figs/plt_radar.png")

Pairplot

In [64]:
iris.columns
Out[64]:
Index(['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Name'], dtype='object')
In [30]:
sns.pairplot(iris, diag_kind="kde", hue="Name")
plt.savefig("figs/sns_pairplot.png")
plt.show()

Combining and styling plots

Plotting styles

In [31]:
plt.style.available
Out[31]:
['fast',
 'tableau-colorblind10',
 'seaborn-paper',
 'seaborn-deep',
 'grayscale',
 'seaborn-colorblind',
 'seaborn-poster',
 'Solarize_Light2',
 'seaborn-white',
 'fivethirtyeight',
 'seaborn',
 'seaborn-whitegrid',
 'seaborn-dark',
 'bmh',
 'dark_background',
 'seaborn-ticks',
 'seaborn-talk',
 '_classic_test',
 'seaborn-muted',
 'seaborn-darkgrid',
 'seaborn-dark-palette',
 'seaborn-notebook',
 'seaborn-bright',
 'seaborn-pastel',
 'ggplot',
 'classic',
 'LHCb']
In [32]:
with plt.style.context('ggplot'):
    plt.plot(x, y)
    plt.savefig("figs/plt_line_ggplot.pdf")
    plt.show()
In [33]:
# plt.style.use("LHCb")
# with plt.style.context('LHCb'):
#     plt.plot(x, y)
#     plt.savefig("figs/plt_line_lhcb.pdf")
#     plt.show()

Subplots

In [62]:
np.random.seed(42)
x = np.arange(0, 10, 0.01)
y = np.random.randn(len(x)).cumsum()
d = np.diff(y)
In [59]:
plt.subplot?
In [35]:
plt.subplot(2, 2, 1)
plt.plot(x, y)

plt.subplot(224)
plt.hist(d, bins=20, density=True)

plt.savefig("figs/plt_subplot.png")
plt.show()
In [60]:
plt.subplots?
In [36]:
fig, axes = plt.subplots(2, 2)
axes[0,0].plot(x, y)
axes[1,1].hist(d, bins=20, density=True)
plt.savefig("figs/plt_subplots.png")
plt.show()

Picture-in Picture

In [63]:
plt.plot(x, y)
plt.axes([0.2, .6, .2, .2])
plt.hist(d, bins=20, density=True)
plt.xticks([])
plt.yticks([])
plt.savefig("figs/plt_pip.png")
plt.show()

Multiple axes

In [38]:
plt.figure()
x = np.linspace(-5, 5)
y = 2*x + 3
y2 = x**2

ax1 = plt.gca()
ax1.plot(x, y)
ax1.set_ylabel("Linear")
ax2 = ax1.twinx()
ax2.plot(x, y2)
ax2.set_ylabel("Quadratic")

plt.savefig("figs/plt_twinx.png")
plt.show()

Networks and Natural Language Processing (NLP)

In [14]:
import networkx as nx
import nltk
from nltk.util import ngrams
import matplotlib

from collections import Counter, defaultdict
from operator import itemgetter
In [4]:
G = nx.cubical_graph()
nx.draw(G)
plt.savefig("figs/networkx_cubical.png")
plt.show()
/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: 
The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.
  if not cb.iterable(width):
In [5]:
ignore = {",", ".", "``", "''", "'", "'s", "?", "!", "-", "--", "...", ";"}

def get_words(file_name, encoding="utf-8"):
    with open(file_name, encoding=encoding) as f:
        for line in f:
            if not line:
                continue
            for word in nltk.word_tokenize(line.strip()):
                if word not in ignore:
                    yield word.lower()
In [6]:
words = list(get_words("data/Harry Potter and the Sorcerer.txt", 'iso-8859-1'))
In [7]:
counter = Counter(words)
w, c = zip(*counter.most_common(15))
c = np.array(c) / len(words) * 100.
In [8]:
plt.plot(w, c)
plt.ylabel("Relative frequency [%]")
plt.show()
In [9]:
words = list(filter(lambda word: len(word) > 3, get_words("data/Harry Potter and the Sorcerer.txt", 'iso-8859-1')))
counter = Counter(words)
In [10]:
from itertools import islice

$\phi=\frac{n_{11}n_{00}-n_{10}n_{01}}{\sqrt{n_{1\cdot}n_{0\cdot}n_{\cdot0}n_{\cdot1}}}$

In [ ]:
def phi(word1, word2, counter, digram_graph):
    n1tot = 
    n11 = digram_graph[word1][word2]
    n00 = sum(counter.values()) - 
In [15]:
g = nx.Graph()
g.add_nodes_from((word, {"size": freq}) for word, freq in counter.items())

digram_graph = defaultdict(Counter)
for word1, word2 in ngrams(words, 2):
    digram_graph[word1][word2] += 1
    digram_graph[word2][word1] += 1

digrams = Counter(tuple(sorted(x)) for x in ngrams(words, 2)).items()

g.add_edges_from((*pair, {"weight": freq / counter[pair[0]]}) for pair, freq in digrams)
In [16]:
digram_graph
Out[16]:
defaultdict(collections.Counter,
            {'harry': Counter({'potter': 40,
                      'their': 3,
                      'dursley': 1,
                      'called': 7,
                      'come': 5,
                      'never': 13,
                      'howard': 1,
                      'nasty': 3,
                      'could': 48,
                      'kill': 3,
                      'heaven': 1,
                      'survive': 1,
                      'bring': 1,
                      'aunt': 2,
                      'sweets': 1,
                      'known': 2,
                      'about': 9,
                      'every': 1,
                      'hiding': 1,
                      'underneath': 1,
                      'took': 8,
                      'arms': 2,
                      'over': 5,
                      'gave': 4,
                      'little': 3,
                      'live': 1,
                      'laid': 1,
                      'gently': 1,
                      'inside': 4,
                      'blankets': 1,
                      'luck': 3,
                      'murmured': 2,
                      'happen': 1,
                      'voices': 1,
                      'house': 3,
                      'noise': 1,
                      'woke': 4,
                      'screeched': 1,
                      'heard': 11,
                      'said': 163,
                      'well': 6,
                      'birthday': 2,
                      'groaned': 1,
                      'forgotten': 2,
                      'slowly': 3,
                      'them': 14,
                      'used': 1,
                      'mystery': 1,
                      'dudley': 8,
                      'punching': 2,
                      'catch': 3,
                      'look': 8,
                      'cupboard': 3,
                      'always': 2,
                      'than': 3,
                      'thin': 1,
                      'thing': 4,
                      'liked': 1,
                      'kitchen': 1,
                      'turning': 2,
                      'that': 37,
                      'needed': 1,
                      'haircut': 1,
                      'must': 5,
                      'place': 1,
                      'frying': 1,
                      'angel': 1,
                      'often': 1,
                      'like': 8,
                      'plates': 1,
                      'face': 9,
                      'while': 3,
                      'uncle': 2,
                      'head': 8,
                      'direction': 2,
                      'horror': 4,
                      'heart': 5,
                      'year': 2,
                      'left': 8,
                      'away': 7,
                      'hated': 2,
                      'furiously': 3,
                      'though': 9,
                      'this': 12,
                      'knew': 11,
                      'here': 6,
                      'hopefully': 1,
                      'they': 12,
                      'shot': 1,
                      'later': 4,
                      'taken': 4,
                      'aside': 2,
                      'close': 4,
                      'warning': 2,
                      'honestly..': 1,
                      'around': 8,
                      'just': 13,
                      'tired': 1,
                      'coming': 2,
                      'silly': 1,
                      'spent': 3,
                      'would': 10,
                      'relief': 2,
                      'punished': 1,
                      'much': 3,
                      'surprise': 3,
                      'from': 7,
                      'headmistress': 1,
                      'been': 7,
                      'doors': 1,
                      'supposed': 2,
                      'work': 1,
                      'council': 2,
                      'bank': 2,
                      'were': 15,
                      'remembering': 2,
                      'yelled': 4,
                      'only': 6,
                      'asked': 24,
                      'what': 21,
                      'either': 1,
                      'thought': 24,
                      'blond': 1,
                      'best': 4,
                      'another': 6,
                      'allowed': 2,
                      'first': 4,
                      'felt': 24,
                      'moved': 5,
                      'with': 20,
                      'winked': 2,
                      'stared': 10,
                      'know': 17,
                      'anyway': 4,
                      'glass': 1,
                      'peered': 2,
                      'again': 4,
                      'read': 4,
                      'behind': 4,
                      'made': 7,
                      'ribs': 1,
                      'fell': 4,
                      'gasped': 10,
                      'past': 2,
                      'gibber': 1,
                      'seen': 4,
                      'worst': 1,
                      'least': 1,
                      'enough': 2,
                      'talking': 1,
                      'starting': 2,
                      'angry': 2,
                      'brandy': 1,
                      'dark': 2,
                      'younger': 1,
                      'dreamed': 1,
                      'asking': 1,
                      'second': 3,
                      'tried': 11,
                      'school': 5,
                      'everybody': 1,
                      'earned': 1,
                      'longest-ever': 1,
                      'crutches': 1,
                      'glad': 2,
                      'sport': 1,
                      'hunting': 1,
                      'there': 12,
                      'other': 7,
                      'told': 18,
                      'want': 6,
                      'poor': 1,
                      'leaving': 2,
                      'mrs.': 1,
                      'before': 7,
                      'watch': 4,
                      'grown-up': 1,
                      'trust': 1,
                      'when': 16,
                      'went': 11,
                      'looked': 25,
                      'finished': 3,
                      'seriously': 1,
                      'uniform': 1,
                      'make': 4,
                      'mail': 4,
                      'dodged': 3,
                      'letter': 5,
                      'harry': 32,
                      'picked': 5,
                      'trembling': 1,
                      'purple': 1,
                      'joke': 1,
                      'suddenly': 10,
                      'something': 7,
                      'point': 1,
                      'trying': 5,
                      'envelope': 1,
                      'move': 3,
                      'both': 3,
                      'glasses': 2,
                      'visited': 1,
                      'moment': 2,
                      'angrily': 1,
                      'painful': 1,
                      'questions': 2,
                      'trip': 1,
                      'sighed': 1,
                      'back': 13,
                      'thinking': 1,
                      'nice': 1,
                      'hall': 2,
                      'right': 8,
                      'grabbed': 3,
                      'wheezed': 1,
                      'walked': 5,
                      'morning': 2,
                      'turned': 11,
                      'door': 5,
                      'leapt': 2,
                      'realized': 3,
                      'exactly': 1,
                      'shouted': 4,
                      'shuffled': 1,
                      'vernon': 2,
                      'arrived': 3,
                      'letters': 1,
                      'found': 5,
                      'amazement': 4,
                      'ducked': 1,
                      'seized': 3,
                      'shared': 1,
                      'snored': 1,
                      'stayed': 1,
                      'cokeworth': 1,
                      'reminded': 4,
                      'tuesday': 1,
                      'eleventh': 1,
                      'privately': 2,
                      'eleven': 1,
                      'minutes': 3,
                      'shivered': 3,
                      'bolt': 1,
                      'giant': 1,
                      'squashed': 1,
                      'opened': 3,
                      'written': 1,
                      'icing': 1,
                      'shook': 8,
                      'whole': 1,
                      'light': 3,
                      'sausages': 1,
                      'hungry': 2,
                      'hagrid': 16,
                      'sorry': 4,
                      'anything': 6,
                      'wildly': 1,
                      'fixing': 1,
                      'eagerly': 2,
                      'wizard': 3,
                      'stretched': 1,
                      'given': 3,
                      'telephone': 1,
                      'interested': 1,
                      'gone': 3,
                      'scandal': 1,
                      'happened': 1,
                      'gargoyles': 1,
                      'people': 1,
                      'down': 6,
                      'suggested': 3,
                      'days': 1,
                      'famous': 2,
                      'ever': 3,
                      'going': 4,
                      'mind': 5,
                      'jumped': 6,
                      'floor': 4,
                      'meanwhile': 1,
                      'question': 2,
                      'disappeared': 2,
                      'eyes': 8,
                      'instead': 1,
                      'constrictor': 1,
                      'curly': 1,
                      'under': 3,
                      'threw': 4,
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             'kicked': Counter({'argue': 1,
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                      'broom': 1,
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                      'engine': 1,
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             'engine': Counter({'drifted': 1,
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             'rose': Counter({'ball': 1,
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             'reply': Counter({'dumbledore': 1, 'nose': 1}),
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                      'wizard': 1,
                      'wrist': 1,
                      'wristwatches': 1}),
             'balls': Counter({'black': 1,
                      'different-sized': 1,
                      'four': 2,
                      'golf': 2,
                      'hard': 1,
                      'harder': 1,
                      'identical': 1,
                      'left': 1,
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                      'puff': 1,
                      'right': 1,
                      'sorta': 1,
                      'string': 1,
                      'three': 1,
                      'twelve': 1}),
             'lamps': Counter({'street': 1, 'that': 1}),
             'glowed': Counter({'drive': 1, 'scarlet': 1, 'suddenly': 2}),
             'orange': Counter({'bulging': 1,
                      'could': 1,
                      'eyes': 1,
                      'knickerbockers': 1,
                      'puff': 1,
                      'suddenly': 1,
                      'tailcoats': 1,
                      'with': 1}),
             'make': Counter({"'gar": 1,
                      'again': 2,
                      'behind': 1,
                      'break': 1,
                      'chessmen': 1,
                      'clean': 1,
                      'coils': 1,
                      'could': 7,
                      'dare': 1,
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                      'drinker': 1,
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                      'dursleys': 1,
                      'easier': 1,
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                      'fool': 2,
                      'forgetfulness': 1,
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                      'here': 1,
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                      'himself': 2,
                      'horrible': 1,
                      'invisible': 1,
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                      'levi-o-sa': 1,
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                      'magnificent': 1,
                      'managed': 2,
                      'more': 1,
                      'move': 1,
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                      'personally': 1,
                      'pineapple': 1,
                      'players': 1,
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                      'remember': 1,
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                      'shortcuts': 1,
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                      'soon': 1,
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                      'sure': 7,
                      'tabby': 1,
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                      'trying': 2,
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                      'winning': 1,
                      'work': 1,
                      'wormwood': 1,
                      'would': 1,
                      'yerselves': 1,
                      'yesterday': 1,
                      'your': 2}),
             'slinking': Counter({'around': 1, 'tabby': 1}),
             'step': Counter({'backward': 1,
                      'blankets': 1,
                      'firs': 1,
                      'forward': 2,
                      'front': 1,
                      'halfway': 1,
                      'library': 1,
                      'mind': 1,
                      'number': 1,
                      'ping': 1,
                      'privet': 1,
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                      'spring': 1,
                      'take': 2,
                      'taken': 1,
                      'taking': 1,
                      'took': 2,
                      'toward': 3,
                      'vanishing': 1}),
             'luck': Counter({'believe': 1,
                      'breaking': 1,
                      'bullet': 1,
                      'good': 5,
                      'grew': 1,
                      'harry': 3,
                      'more': 1,
                      'outside': 1,
                      'pushing': 1,
                      'sitting': 1,
                      'take': 1,
                      'weasley': 1,
                      'wingardium': 1,
                      'your': 1}),
             'murmured': Counter({'firenze': 1,
                      'hagrid': 2,
                      'harry': 2,
                      'holding': 1,
                      'knew': 1,
                      'look': 1,
                      'quirrell': 1,
                      'reached': 1,
                      'tapping': 1,
                      'there': 1,
                      'through': 1,
                      'turned': 1}),
             'heel': Counter({'carrying': 1,
                      'fang': 1,
                      'turned': 1,
                      'with': 1}),
             'swish': Counter({'cloak': 1,
                      'flick': 2,
                      'remember': 1,
                      'usual': 1,
                      'with': 1}),
             'breeze': Counter({'gone': 1,
                      'lifted': 1,
                      'light': 1,
                      'ruffled': 1}),
             'neat': Counter({'fields': 1,
                      'hedges': 1,
                      'lines': 1,
                      'lying': 1,
                      'pass': 1,
                      'ruffled': 1,
                      'straight': 1,
                      'tail': 1,
                      'there': 1,
                      'wilder': 1}),
             'hedges': Counter({'neat': 1, 'privet': 1}),
             'silent': Counter({'ahern': 1,
                      'black': 1,
                      'bless': 1,
                      'class': 1,
                      'everyone': 1,
                      'fell': 3,
                      'fight': 1,
                      'furious': 1,
                      'little': 1,
                      'staring': 1,
                      'still': 1,
                      'that': 1,
                      'there': 1,
                      'tidy': 1,
                      'which': 1,
                      'without': 1}),
             'tidy': Counter({'front': 1, 'same': 1, 'silent': 1, 'under': 1}),
             'inky': Counter({'under': 1, 'very': 1}),
             'astonishing': Counter({'expect': 1,
                      'powers': 1,
                      'reveal': 1,
                      'sight': 1,
                      'things': 1,
                      'with': 1}),
             'happen': Counter({'anyone': 1,
                      'could': 1,
                      'going': 1,
                      'hagrid': 1,
                      'harry': 1,
                      'might': 1,
                      'once': 1,
                      'said': 1,
                      'seemed': 1,
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                      'that': 1,
                      'them': 2,
                      'they': 1,
                      'things': 3,
                      'wand': 1,
                      'when': 3,
                      'would': 1}),
             'rolled': Counter({'broomstick': 1,
                      'face': 1,
                      'hagrid': 1,
                      'hermione': 1,
                      'hidden': 1,
                      'house': 1,
                      'mcgonagall': 1,
                      'note': 1,
                      'onto': 2,
                      'over': 3,
                      'potter': 1,
                      'quirrell': 1,
                      'scroll': 1,
                      'shaking': 1,
                      'shelling': 1,
                      'simply': 1,
                      'sleeves': 1,
                      'stove': 1,
                      'were': 1}),
             'without': Counter({'along': 1,
                      'another': 1,
                      'anyone': 1,
                      'asking': 1,
                      'away': 1,
                      'began': 1,
                      'being': 1,
                      'better': 2,
                      'blankets': 1,
                      'blowing': 1,
                      'breakfast': 1,
                      'brooms': 1,
                      'buying': 1,
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                      'creepers': 1,
                      'downstairs': 1,
                      'effort': 1,
                      'ending': 1,
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                      'even': 2,
                      'evening': 1,
                      'everyone': 1,
                      'fight': 1,
                      'food': 1,
                      'further': 1,
                      'getting': 1,
                      'give': 1,
                      'glass': 1,
                      'here': 1,
                      'homework': 1,
                      'idea': 2,
                      'lead': 1,
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                      'long': 2,
                      'look': 1,
                      'madam': 1,
                      'magic': 1,
                      'manage': 1,
                      'next': 1,
                      'noticing': 1,
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                      'puffing': 1,
                      'quirrell': 2,
                      'ravenclaw': 1,
                      'realizing': 1,
                      'revenge': 1,
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                      'shop': 1,
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                      'skin': 1,
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                      'tell': 1,
                      'their': 2,
                      'them': 1,
                      'then': 1,
                      'times': 1,
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                      'turning': 1,
                      'voldemort': 1,
                      'waited': 1,
                      'waking': 1,
                      'what': 2,
                      'winning': 1,
                      'word': 1,
                      'words': 1,
                      'worrying': 1,
                      'would': 1}),
             'waking': Counter({'small': 1, 'without': 1}),
             'hand': Counter({'after': 1,
                      'again': 1,
                      'almost': 1,
                      'back': 2,
                      'been': 1,
                      'began': 1,
                      'bitten': 1,
                      'broom': 1,
                      'c-ca': 1,
                      'call': 1,
                      'centaur': 1,
                      'clap': 1,
                      'clapped': 2,
                      'clapping': 1,
                      'clasped': 1,
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                      'closed': 1,
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                      'doing': 1,
                      'door': 1,
                      'enormous': 1,
                      'enough': 1,
                      'favored': 1,
                      'felt': 2,
                      'fitted': 1,
                      'flutter': 1,
                      'foot': 1,
                      'forehead': 1,
                      'glancing': 1,
                      'going': 1,
                      'good': 1,
                      'gotten': 1,
                      'great': 1,
                      'harry': 5,
                      'head': 1,
                      'held': 2,
                      'hermione': 3,
                      'high': 1,
                      'holding': 1,
                      'into': 3,
                      'just': 1,
                      'knight': 1,
                      'knocked': 1,
                      'last': 2,
                      'malfoys': 1,
                      'mcgonagall': 1,
                      'mouth': 1,
                      'once': 1,
                      'only': 1,
                      'other': 6,
                      'over': 2,
                      'perform': 1,
                      'puppet': 1,
                      'quirrell': 1,
                      'quivering': 1,
                      'raised': 2,
                      'rammed': 1,
                      'reached': 2,
                      'red-haired': 1,
                      'remembrall': 1,
                      'right': 1,
                      'room': 1,
                      'rose': 1,
                      'said': 2,
                      'seized': 1,
                      'shake': 1,
                      'shaken': 1,
                      'sharply': 1,
                      'shook': 2,
                      'shot': 1,
                      'shoulder': 1,
                      'small': 1,
                      'smell': 1,
                      'smiled': 1,
                      'snatched': 1,
                      'snitch': 1,
                      'something': 1,
                      'stands': 1,
                      'stood': 1,
                      'street': 1,
                      'stretched': 3,
                      'stretching': 1,
                      'stroke': 1,
                      'struggling': 1,
                      'swollen': 1,
                      'tears': 1,
                      'them': 1,
                      'thing': 1,
                      'thought': 1,
                      'touch': 1,
                      'trembling': 1,
                      'troll': 1,
                      'twenty-four': 1,
                      'uncle': 1,
                      'vigorously': 1,
                      'wand': 1,
                      'want': 2,
                      'waved': 1,
                      'what': 1,
                      'when': 1,
                      'which': 1,
                      'whispered': 1,
                      'with': 3,
                      'woman': 1,
                      'your': 2}),
             'closed': Counter({'away': 1,
                      'eyes': 2,
                      'hagrid': 1,
                      'hand': 1,
                      'letter': 1,
                      'open': 1,
                      'quickly': 1,
                      'turban': 1,
                      'were': 1}),
             'beside': Counter({'burnished': 1,
                      'last': 1,
                      'letter': 1,
                      'slept': 1,
                      'small': 1,
                      'standing': 1,
                      'themselves': 1,
                      'were': 1}),
             'slept': Counter({'beside': 1,
                      'dudley': 1,
                      'knowing': 1,
                      'when': 1,
                      'where': 2}),
             'knowing': Counter({'about': 1,
                      'begin': 1,
                      'bring': 1,
                      'broomstick': 1,
                      'driven': 1,
                      'everybody': 1,
                      'famous': 2,
                      'hardly': 1,
                      'mcgonagall': 1,
                      'slept': 1,
                      'special': 2,
                      'wand': 1,
                      'what': 4,
                      'worth': 1,
                      'would': 1}),
             ...})
In [48]:
def filter_edges(g, most_common_nodes, cutoff=0.5):
    most_common_nodes = set(most_common_nodes)
    for edge in g.edges:
        if not all(node in most_common_nodes for node in edge):
            continue
        if g.get_edge_data(*edge)["weight"] < cutoff:
            continue
        yield edge
In [49]:
most_common_nodes = list(map(itemgetter(0), counter.most_common(100)))
edges = list(filter_edges(g, most_common_nodes, cutoff=0.))
In [50]:
g2 = nx.Graph()
g2.add_nodes_from((node, g.node[node]) for node in most_common_nodes)
g2.add_edges_from((*edge, g.get_edge_data(*edge)) for edge in edges)
In [51]:
plt.figure(figsize=(20, 20))
layout = nx.spring_layout(g2, k=0.1, iterations=50)
sizes = [g2.node[node]["size"] * 5 for node in g2]
edge_weights = np.array([g2.get_edge_data(*edge)["weight"] for edge in g2.edges])
nx.draw(g2, pos=layout,
        with_labels=True, node_size=sizes, node_color="#cccccc",
        edge_cmap=plt.cm.Blues, edge_color=edge_weights, edge_vmin=edge_weights.min(), edge_vmax=edge_weights.max())
/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: 
The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.
  if not cb.iterable(width):
/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:585: MatplotlibDeprecationWarning: 
The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.
  and cb.iterable(edge_color) \
/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:595: MatplotlibDeprecationWarning: 
The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.
  for c in edge_color]):
In [52]:
plt.hist(np.log(edge_weights))
plt.show()