بصری سازی

#Creating the dataset

df = sns.load_dataset(‘titanic’)

df=df.groupby(‘who’)[‘fare’].sum().to_frame().reset_index()

#Creating the bar chart

plt.barh(df[‘who’],df[‘fare’],color = [‘#F0F8FF’,’#E6E6FA’,’#B0E0E6′])

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Creating bar plot

sns.barplot(x = ‘fare’,y = ‘who’,data = titanic_dataset,palette = “Blues”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

df = sns.load_dataset(‘titanic’)

df=df.groupby(‘who’)[‘fare’].sum().to_frame().reset_index()

#Creating the column plot

plt.bar(df[‘who’],df[‘fare’],color = [‘#F0F8FF’,’#E6E6FA’,’#B0E0E6′])

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Reading the dataset

titanic_dataset = sns.load_dataset(‘titanic’)

#Creating column chart

sns.barplot(x = ‘who’,y = ‘fare’,data = titanic_dataset,palette = “Blues”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

df = sns.load_dataset(‘titanic’)

df_pivot = pd.pivot_table(df, values=”fare”,index=”who”,columns=”class”, aggfunc=np.mean)

#Creating a grouped bar chart

ax = df_pivot.plot(kind=”bar”,alpha=0.5)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Reading the dataset

titanic_dataset = sns.load_dataset(‘titanic’)

#Creating the bar plot grouped across classes

sns.barplot(x = ‘who’,y = ‘fare’,hue = ‘class’,data = titanic_dataset, palette = “Blues”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

# Stacked bar chart

#Creating the dataset

df = pd.DataFrame(columns=[“A”,”B”, “C”,”D”],

                  data=[[“E”,0,1,1],

                        [“F”,1,1,0],

                        [“G”,0,1,0]])

df.plot.bar(x=’A’, y=[“B”, “C”,”D”],  stacked=True,  width = 0.4,alpha=0.5)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

dataframe = pd.DataFrame(columns=[“A”,”B”, “C”,”D”],

                  data=[[“E”,0,1,1],

                        [“F”,1,1,0],

                        [“G”,0,1,0]])

dataframe.set_index(‘A’).T.plot(kind=’bar’, stacked=True)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

df = sns.load_dataset(“iris”)

df=df.groupby(‘sepal_length’)[‘sepal_width’].sum().to_frame().reset_index()

#Creating the line chart

plt.plot(df[‘sepal_length’], df[‘sepal_width’])

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Creating the dataset

cars = [‘AUDI’, ‘BMW’, ‘NISSAN’,

        ‘TESLA’, ‘HYUNDAI’, ‘HONDA’]

data = [20, 15, 15, 14, 16, 20]

#Creating the pie chart

plt.pie(data, labels = cars,colors = [‘#F0F8FF’,’#E6E6FA’,’#B0E0E6′,’#7B68EE’,’#483D8B’])

#Adding the aesthetics

plt.title(‘Chart title’)

#Show the plot

plt.show()

#Creating the dataset

cars = [‘AUDI’, ‘BMW’, ‘NISSAN’,

        ‘TESLA’, ‘HYUNDAI’, ‘HONDA’]

data = [20, 15, 15, 14, 16, 20]

#Creating the pie chart

plt.pie(data, labels = cars,colors = [‘#F0F8FF’,’#E6E6FA’,’#B0E0E6′,’#7B68EE’,’#483D8B’])

#Adding the aesthetics

plt.title(‘Chart title’)

#Show the plot

plt.show()

#Reading the dataset

x=range(1,6)

y=[ [1,4,6,8,9], [2,2,7,10,12], [2,8,5,10,6] ]

#Creating the area chart

ax = plt.gca()

ax.stackplot(x, y, labels=[‘A’,’B’,’C’],alpha=0.5)

#Adding the aesthetics

plt.legend(loc=’upper left’)

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

# Data

years_of_experience =[1,2,3]

salary=[ [6,8,10], [4,5,9], [3,5,7] ]

# Plot

plt.stackplot(years_of_experience,salary, labels=[‘Company A’,’Company B’,’Company C’])

plt.legend(loc=’upper left’)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

penguins = sns.load_dataset(“penguins”)

#Creating the column histogram

ax = plt.gca()

ax.hist(penguins[‘flipper_length_mm’], color=’blue’,alpha=0.5, bins=10)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Reading the dataset

penguins_dataframe = sns.load_dataset(“penguins”)

#Plotting bar histogram

sns.distplot(penguins_dataframe[‘flipper_length_mm’], kde=False, color=’blue’, bins=10)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

df_1 = np.random.normal(0, 1, (1000, ))

density = stats.gaussian_kde(df_1)

#Creating the line histogram

n, x, _ = plt.hist(df_1, bins=np.linspace(-3, 3, 50), histtype=u’step’, density=True)

plt.plot(x, density(x))

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Reading the dataset

penguins_dataframe = sns.load_dataset(“penguins”)

#Plotting line histogram

sns.distplot(penguins_dataframe[‘flipper_length_mm’], hist = False, kde = True, label=’Africa’)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

df = sns.load_dataset(“tips”)

#Creating the scatter plot

plt.scatter(df[‘total_bill’],df[‘tip’],alpha=0.5 )

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Reading the dataset

bill_dataframe = sns.load_dataset(“tips”)

#Creating scatter plot

sns.scatterplot(data=bill_dataframe, x=”total_bill”, y=”tip”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Creating the dataset

np.random.seed(42)

N = 100

x = np.random.normal(170, 20, N)

y = x + np.random.normal(5, 25, N)

colors = np.random.rand(N)

area = (25 * np.random.rand(N))**2

df = pd.DataFrame({

    ‘X’: x,

    ‘Y’: y,

    ‘Colors’: colors,

    “bubble_size”:area})

#Creating the bubble chart

plt.scatter(‘X’, ‘Y’, s=’bubble_size’,alpha=0.5, data=df)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

#Reading the dataset

bill_dataframe = sns.load_dataset(“tips”)

#Creating bubble plot

sns.scatterplot(data=bill_dataframe, x=”total_bill”, y=”tip”, hue=”size”, size=”size”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

from past.builtins import xrange

#Creating the dataset

df_1 = [[1,2,5], [5,7,2,2,5], [7,2,5]]

df_2 = [[6,4,2], [1,2,5,3,2], [2,3,5,1]]

#Creating the box plot

ticks = [‘A’, ‘B’, ‘C’]

plt.figure()

bpl = plt.boxplot(df_1, positions=np.array(xrange(len(df_1)))*2.0-0.4, sym=”, widths=0.6)

bpr = plt.boxplot(df_2, positions=np.array(xrange(len(df_2)))*2.0+0.4, sym=”, widths=0.6)

plt.plot([], c=’#D7191C’, label=’Label 1′)

plt.plot([], c=’#2C7BB6′, label=’Label 2′)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

plt.legend()

plt.xticks(xrange(0, len(ticks) * 2, 2), ticks)

plt.xlim(-2, len(ticks)*2)

plt.ylim(0, 8)

plt.tight_layout()

#Show the plot

plt.show()

#Reading the dataset

bill_dataframe = sns.load_dataset(“tips”)

#Creating boxplots

ax = sns.boxplot(x=”day”, y=”total_bill”, hue=”smoker”, data=bill_dataframe, palette=”Set3″)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Reading the dataset

test = pd.Series(-1 + 2 * np.random.rand(10), index=list(‘abcdefghij’))

#Function for makig a waterfall chart

def waterfall(series):

    df = pd.DataFrame({‘pos’:np.maximum(series,0),’neg’:np.minimum(series,0)})

    blank = series.cumsum().shift(1).fillna(0)

    df.plot(kind=’bar’, stacked=True, bottom=blank, color=[‘r’,’b’], alpha=0.5)

    step = blank.reset_index(drop=True).repeat(3).shift(-1)

    step[1::3] = np.nan

    plt.plot(step.index, step.values,’k’)

#Creating the waterfall chart

waterfall(test)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

#Show the plot

plt.show()

from matplotlib_venn import venn3

#Making venn  diagram

venn3(subsets = (10, 8, 22, 6,9,4,2))

plt.show()

import squarify

sizes = [40, 30, 5, 25, 10]

squarify.plot(sizes)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

# Show the plot

plt.show()

#Reading the dataset

r = [0,1,2,3,4]

raw_data = {‘greenBars’: [20, 1.5, 7, 10, 5], ‘orangeBars’: [5, 15, 5, 10, 15],’blueBars’: [2, 15, 18, 5, 10]}

df = pd.DataFrame(raw_data)

# From raw value to percentage

totals = [i+j+k for i,j,k in zip(df[‘greenBars’], df[‘orangeBars’], df[‘blueBars’])]

greenBars = [i / j * 100 for i,j in zip(df[‘greenBars’], totals)]

orangeBars = [i / j * 100 for i,j in zip(df[‘orangeBars’], totals)]

blueBars = [i / j * 100 for i,j in zip(df[‘blueBars’], totals)]

# plot

barWidth = 0.85

names = (‘A’,’B’,’C’,’D’,’E’)

# Create green Bars

plt.bar(r, greenBars, color=’#b5ffb9′, edgecolor=’white’, width=barWidth)

# Create orange Bars

plt.bar(r, orangeBars, bottom=greenBars, color=’#f9bc86′, edgecolor=’white’, width=barWidth)

# Create blue Bars

plt.bar(r, blueBars, bottom=[i+j for i,j in zip(greenBars, orangeBars)], color=’#a3acff’, edgecolor=’white’, width=barWidth)

# Custom x axis

plt.xticks(r, names)

plt.xlabel(“group”)

#Adding the aesthetics

plt.title(‘Chart title’)

plt.xlabel(‘X axis title’)

plt.ylabel(‘Y axis title’)

plt.show()

#Reading the dataset

iris_dataframe = sns.load_dataset(‘iris’)

#Creating marginal graphs

sns.jointplot(x=iris_dataframe[“sepal_length”], y=iris_dataframe[“sepal_width”], kind=’scatter’)

# Show the plot

plt.show()

#Creating the dataset

df = sns.load_dataset(“iris”)

df=df.groupby(‘sepal_length’)[‘sepal_width’].sum().to_frame().reset_index()

#Creating the subplot

fig, axes = plt.subplots(nrows=2, ncols=2)

ax=df.plot(‘sepal_length’, ‘sepal_width’,ax=axes[0,0])

ax.get_legend().remove()

#Adding the aesthetics

ax.set_title(‘Chart title’)

ax.set_xlabel(‘X axis title’)

ax.set_ylabel(‘Y axis title’)

ax=df.plot(‘sepal_length’, ‘sepal_width’,ax=axes[0,1])

ax.get_legend().remove()

ax=df.plot(‘sepal_length’, ‘sepal_width’,ax=axes[1,0])

ax.get_legend().remove()

ax=df.plot(‘sepal_length’, ‘sepal_width’,ax=axes[1,1])

ax.get_legend().remove()

#Show the plot

plt.show()