11 Less Used but Important Plots for Data Science

import plotly.express as px df = px.data.iris() df

import plotly.express as px df = px.data.iris() fig = px.parallel_coordinates(df, color="species_id", labels={"species_id": "Species", "sepal_width": "Sepal Width", "sepal_length": "Sepal Length", "petal_width": "Petal Width", "petal_length": "Petal Length", }, color_continuous_scale=px.colors.diverging.Tealrose, color_continuous_midpoint=2) fig.show()

# import numpy as np # import seaborn as sns # sns.set_theme(style="ticks") # rs = np.random.RandomState(11) # x = rs.gamma(2, size=1000) # y = -.5 * x + rs.normal(size=1000) # sns.jointplot(x=x, y=y, kind="hex", color="#4CB391")

ax = df.plot.hexbin(x='sepal_width', y='sepal_length', gridsize=20,color='#BDE320')

import plotly.express as px fig = px.density_contour(df, x="sepal_width", y="sepal_length") fig.update_traces(contours_coloring="fill", contours_showlabels = True) fig.show()

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

np.random.seed(10) # Generate Univariate Observations gauss_data = 5 * np.random.randn(100) + 50

sns.histplot(data=gauss_data, kde=True)

!pip install statsmodels

import statsmodels.api as sm # q-q plot sm.qqplot(gauss_data, line='s') plt.show()

import seaborn as sns sns.violinplot(data=df, y="sepal_width")

import seaborn as sns sns.violinplot(data=df,x='species', y="sepal_width")

sns.boxenplot(x=df["sepal_width"])

sns.boxenplot(data=df, x="species",y='sepal_width')

import seaborn as sns sns.pointplot(data=df,x="species", y="sepal_width")

import seaborn as sns sns.swarmplot(data=df,x="species", y="sepal_width")

!pip install wordcloud

import pandas as pd data=pd.read_csv('/work/android-games.csv') data.head()

data.category.value_counts()

#importing the module from wordcloud library from wordcloud import WordCloud import matplotlib.pyplot as plt #creating a text from the category column by taking only the 2nd part of the category. text = " ".join(cat.split()[1] for cat in data.category) #generating the cloud word_cloud = WordCloud(collocations = False, background_color = 'black').generate(text) plt.imshow(word_cloud, interpolation='bilinear') plt.axis("off") plt.show()

import plotly.express as px df = px.data.tips()

df.head(5)

fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='time') fig.show()