aki_prj23_transparenzregister/documentations/seminararbeiten/Datenvisualisierung/Diagramme_plotly/Visualisierung_plotly.ipynb

Erstellung verschiedener Diagrammarten mit Plotly¶

Installiere Plotly, beispielsweise mit pip:

In [417]:

# install plotly using pip
!pip3.10 install plotly

Requirement already satisfied: plotly in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (5.14.1)
Requirement already satisfied: tenacity>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from plotly) (8.2.2)
Requirement already satisfied: packaging in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from plotly) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (from packaging->plotly) (3.0.6)

Zum statischen Speichern der Plotly Figuren, installiere Kaleido:

In [418]:

!pip3.10 install -U kaleido

Requirement already satisfied: kaleido in /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages (0.2.1)

Generierung von Beispieldaten und Anlegen eines Pandas-Dataframes:

In [419]:

# import libraries
import plotly.express as px
import plotly.graph_objs as go
import pandas as pd
import numpy as np

# create sample data for one company
data = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],
        'Umsatz': [19, 23, 30, 42, 37, 45]}
# save as pandas dataframe
df = pd.DataFrame.from_dict(data)

# create sample data for a second company
data2 = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],
        'Umsatz': [15, 21, 33, 28, 27, 30]}
# save as pandas dataframe
df2 = pd.DataFrame.from_dict(data2)

Säulendiagramme¶

Erstellung eines "einfachen" Säulendiagramms:

In [420]:

# create bar plot
fig_saeule = px.bar(df, y = 'Umsatz', labels = {'index': '', 'Umsatz': ''})
# set color
fig_saeule.update_traces(marker_color = '#00509b')

# save as image
fig_saeule.write_image('Saeule.png')
# show in notebook
fig_saeule.show()

Erstellung eines "einfachen" Balkendiagramms:

In [421]:

# create horizontal bar plot
fig_balken = px.bar(df, x = 'Umsatz', labels = {'index': '', 'Umsatz': ''}, orientation='h')
# set color
fig_balken.update_traces(marker_color = '#00509b')

# save as image
fig_balken.write_image('Balken.png')
# show in notebook
fig_balken.show()

Ergänzung von Titel und Achsenbeschriftungen:

In [422]:

# sreate bar plot with named labels and title
fig_saeule_titel = px.bar(df, x = 'Jahr', y = 'Umsatz', labels = {'Umsatz': 'Umsatz in Mio.€'}, title = 'Umsatzentwicklung von Unternehmen A')
# set color
fig_saeule_titel.update_traces(marker_color = '#00509b')

# save as image
fig_saeule_titel.write_image('Saeule_Titel.png')
# show in notebook
fig_saeule_titel.show()

Vergleich zweier Datensätze in einem Säulendiagramm:

In [423]:

# create figure
fig_saeule_zwei= go.Figure()

# add trace for company 1
fig_saeule_zwei.add_trace(go.Bar(x = df['Jahr'], y = df['Umsatz'], name = 'A', marker_color="#00509b"))

# add trace for company 2
fig_saeule_zwei.add_trace(go.Bar(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', marker_color = "#6f7072"))

# update layout to grouped
fig_saeule_zwei.update_layout(barmode='group')

# set title and labels
fig_saeule_zwei.update_layout(
    title = "Vergleich der Umsatzentwicklung",
    xaxis_title = "Jahr",
    yaxis_title = "Umsatz in Mio.€",
    legend_title = "Unternehmen",
)

# save as image
fig_saeule_zwei.write_image('Saeule_Zwei.png')
# show in notebook
fig_saeule_zwei.show()

Liniendiagramme¶

Erstellen eines "einfachen" Liniendiagramms:

In [424]:

# create line plot
fig_line = px.line(df, y = df['Umsatz'], labels = {'index':'', 'Umsatz': ''})
# set color
fig_line.update_traces(line_color = '#00509b')
# save as image
fig_line.write_image("Linie.png")
# show in network
fig_line.show()

In [425]:

# create figure
fig_line = go.Figure()
# add trace for company 1
fig_line.add_trace(go.Scatter(x = df['Jahr'], y = df['Umsatz'], name = 'A', line_color = '#00509b', marker_color = '#00509b'))
# add trace for company 2
fig_line.add_trace(go.Scatter(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', line_color = '#6f7072', marker_color = '#6f7072'))
# set title and labels
fig_line.update_layout(
    title = "Vergleich der Umsatzentwicklung",
    xaxis_title = "Jahr",
    yaxis_title = "Umsatz in Mio.€",
    legend_title = "Unternehmen",
)
# save as image
fig_line.write_image("Linie_Vergleich.png")
# show in notebook
fig_line.show()

Streudiagramm¶

In [426]:

# create sample data
x = [1,2,2,3,5,5,6,6,6,7,8,8,8,10,10,10,7,4,3,4,9,6]
y = [1,2,3,3,3,4,5,5,6,6,7,7,8,8,9,10,2,10,8,6,6,4]

# create scatter plot
scatter = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker = {'color': '#00509b'}))
# save as image
scatter.write_image('Streudiagramm.png')
# show in netbook
scatter.show()

Kreisdiagramm¶

Erstellung eines "einfachen" Kreisdiagramms:

In [427]:

# create sample data
sentiment = [0.1, 0.3, 0.6]
scores = ['negativ', 'neutral', 'positiv']
# create pie chart
fig_kreis = px.pie(values=sentiment, color = scores, color_discrete_map={'negativ':'black',
                                 'neutral':'#6f7072',
                                 'positiv':'#00509b'})
# save as image
fig_kreis.write_image('Kreis.png')
# show in notebook
fig_kreis.show()

Erweiterung um geeignete Farben, Beschriftungen und einen Titel:

In [428]:

# create figure 
fig_sentiment = px.pie(values=sentiment, names=scores, color = scores, color_discrete_map={'negativ':'lightcoral',
                                 'neutral':'moccasin',
                                 'positiv':'darkseagreen'}, title = 'Stimmungsanalyse basierend auf Nachrichtenartikel X')
# change line color
fig_sentiment.update_traces(marker = dict(line=dict(color='#000000', width=2)))
# save as image
fig_sentiment.write_image('Kreis_Sentiment.png')
# show in notebook
fig_sentiment.show()

Pfeile¶

Achtung: Berechnungen stimmen noch nicht, Beispiel funktioniert bisher nur für 0.75 und 0.25 einigermaßen

In [429]:

import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np

compound = 0.75
angle = (compound * 100 - 50) * 1.8

x_length = (np.sin(np.radians(angle))*1.2)/2
y_length = (np.cos(np.radians(angle))*1.2)/2

if compound < 0.5:
    y_tail = y_length
    y_head = -y_length
    x_tail = x_length
    x_head = - x_length
else:
    y_tail = -y_length
    y_head = y_length
    x_tail = - x_length
    x_head = x_length


dx = x_head - x_tail
dy = y_head - y_tail

fig, ax = plt.subplots() 
arrow = patches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),
                                 mutation_scale=100, ec = 'darkseagreen', fc = 'darkseagreen')
ax.add_patch(arrow)
plt.axis('off')
ax.set_xlim([-1, 1])
ax.set_ylim([-1, 1])
ax.set_title('Aktueller Stimmungstrend', fontsize=20)
fig.suptitle(' ', fontsize=24)
fig.savefig('Pfeil.png')