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Competition - crowdfunding marketing
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
df= pd.read_csv('./data/crowdfunding.csv')
# total donation amount
total_amount = int(df.amount.sum())
# total donors
total_donors = int(len(df.amount))
# highest donation amount
highest_amount = int(df.amount.max())
# median donation amount
median_amount = int(df.amount.median())
stats = go.Figure()
stats.add_trace(go.Indicator(
mode = "number",
title = {'text': "Total Funds Raised"},
number = {'prefix': "$"},
value = total_amount,
domain = {'row': 0, 'column': 0}))
stats.add_trace(go.Indicator(
mode = "number",
title = {'text': "Number of Donations"},
value = total_donors,
domain = {'row': 0, 'column': 1}))
stats.add_trace(go.Indicator(
mode = "number",
number = {'prefix': "$"},
title = {'text': "Median Donation"},
value = median_amount,
domain = {'row': 1, 'column': 0}))
stats.add_trace(go.Indicator(
mode = "number",
number = {'prefix': "$"},
title = {'text': "Highest Donation"},
value = highest_amount,
domain = {'row': 1, 'column': 1}))
stats.update_layout(
grid = {'rows': 2, 'columns': 2, 'pattern': "independent"},
plot_bgcolor='#333',
paper_bgcolor='#333',
autosize=True,margin={'t': 50,'l':100,'b':50,'r':100},
height=300,
#width=500,
template = {'data' : {'indicator': [{
'title': {"font_color":"#ffffff","font_size":18, "font_family":"Lato, sans-serif"},
'mode' : "number+delta+gauge",
'number' : {"font_color":"#ffffff","font_size":52}}]
}})
stats.show()
# donut chart
fig_donut = px.pie(df, names='device', hole=0.7, title='<b>Most used device by donators',
color_discrete_sequence=['#e2e2e2', '#b1eb1e'])
fig_donut.update_traces(hovertemplate=None, textposition='outside', textinfo='percent+label', rotation=90)
fig_donut.update_layout(margin=dict(t=100, b=30, l=0, r=0),
showlegend=False,
plot_bgcolor='#333',
height=350,
#width=600,
paper_bgcolor='#333',
title_font=dict(size=30,
color='#ffffff',
family="Lato, sans-serif"),
font=dict(size=17, color='#ffffff'),
hoverlabel=dict(bgcolor="#444", font_size=13, font_family="Lato, sans-serif"))
fig_donut.show()
fig_top3 = px.histogram(df,
y='category',
x="amount",
title='<b>The biggest categorie donations',
color="category",
color_discrete_map={
"Sports": "#b1eb1e",
"Games": "#b1eb1e",
"Technology": "#b1eb1e",
"Environment": "#e2e2e2",
"Fashion": "#e2e2e2",
}
)
fig_top3.update_xaxes(visible=False,range = [150000, 170000])
fig_top3.update_yaxes(showgrid=False, ticksuffix=' ', showline=False,categoryorder='total ascending')
fig_top3.update_traces(hovertemplate=None, marker=dict(line=dict(width=0)))
fig_top3.update_layout(margin=dict(t=85, b=20, l=70, r=40),
hovermode="y unified",
showlegend=False,
xaxis_title=' ',
yaxis_title=" ",
plot_bgcolor='#333',
paper_bgcolor='#333',
title_font=dict(size=30,
color='#ffffff',
family="Lato, sans-serif"),
#width=700,
#height=350,
font=dict(color='#ffffff', size=17),
hoverlabel=dict(bgcolor='#333', font_size=13, font_family="Lato, sans-serif"))
fig_top3.show()
# Age proportion per device
fig_age_p = px.histogram(df,
y='age',
histnorm="probability density",
title='<b>Proportion of donations by age and device',
color="device",
barmode="group",
labels={"device":""},
color_discrete_sequence=['#e2e2e2', '#b1eb1e'],
category_orders=dict(age=['18-24', '25-34', '35-44', '45-54', '55+']),
)
fig_age_p.update_xaxes(visible=False)
fig_age_p.update_yaxes(showgrid=False, ticksuffix=' ', showline=False)
fig_age_p.update_traces(hovertemplate=None, marker=dict(line=dict(width=0)))
fig_age_p.update_layout(margin=dict(t=85, b=20, l=70, r=40),
hovermode="y unified",
showlegend=True,
xaxis_title="",
yaxis_title="",
plot_bgcolor='#333',
paper_bgcolor='#333',
title_font=dict(size=30, color='#ffffff', family="Lato, sans-serif"),
#width=700,
#height=450,
legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="center", x=0.5),
font=dict(color='#ffffff', size=15),
hoverlabel=dict(bgcolor='#333', font_size=13, font_family="Lato, sans-serif"))
fig_age_p.show()
# Age distribution
# making a copy of df
dff = df.copy()
# making 2 df one for iOS and another for android with category
df_ios = dff[dff['device']=='iOS'][['age', 'device']].rename(columns={'device':'ios'})
df_android = dff[dff['device']=='android'][['age', 'device']].rename(columns={'device':'android'})
df_ios = pd.DataFrame(df_ios.age.value_counts()).reset_index().rename(columns={'index':'ios'})
df_ios['age_final'] = df_ios['age']
# making age column value negative
df_ios['age'] *= -1
df_android = pd.DataFrame(df_android.age.value_counts()).reset_index().rename(columns={'index':'android'})
fig_age_n = make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_yaxes=True, horizontal_spacing=0)
# bar plot for iOS
fig_age_n.append_trace(go.Bar(x=df_ios.age, y=df_ios.ios, orientation='h', showlegend=True,
text=df_ios.age_final, name='iOS', marker_color='#e2e2e2'), 1, 1)
# bar plot for android
fig_age_n.append_trace(go.Bar(x=df_android.age, y=df_android.android, orientation='h', showlegend=True, text=df_android.age,
name='Android', marker_color='#b1eb1e'), 1, 2)
fig_age_n.update_xaxes(showgrid=False,visible=False)
fig_age_n.update_yaxes(showgrid=False, ticksuffix=' ', showline=False,categoryorder='array', categoryarray= ['55+','45-54','35-44','25-34','18-24'])
fig_age_n.update_traces(hovertemplate=None, marker=dict(line=dict(width=0)),texttemplate='%{text:.2s}')
fig_age_n.update_layout(title='<b>Number of donations across age and device',
margin=dict(t=100, b=0, l=70, r=40),
hovermode="y unified",
xaxis_title="",
yaxis_title="",
plot_bgcolor='#333',
paper_bgcolor='#333',
title_font=dict(size=30, color='#ffffff', family="Lato, sans-serif"),
font=dict(color='#ffffff', size=15),
legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="center", x=0.5),
hoverlabel=dict(bgcolor='#333', font_size=13, font_family="Lato, sans-serif"))
fig_age_n.show()
# Amount distribution
fig_age = px.violin(df, x="amount", color ="device",violinmode="overlay",facet_col="age",
color_discrete_sequence=['#e2e2e2', '#b1eb1e'],
labels=dict(device=""),
category_orders=dict(age=['18-24', '25-34', '35-44', '45-54', '55+']),
facet_col_spacing=0
)
fig_age.update_traces(orientation='h', side='positive', meanline_visible=True, line_width=5, opacity=0.9,
points=False)
fig_age.update_xaxes(showgrid=False,zeroline=False, tickvals=["10","40","80"],title="",range=[0,100])
fig_age.update_yaxes(showgrid=False,zeroline=False,range=[0.01,0.25])
fig_age.update_layout(title='<B>Amount of donations distribution by age and device',
margin=dict(t=140, b=10, l=0, r=30), #l=left,r=ight,t=top,b=botton
width=450,
#height=400,
hovermode="y unified",
xaxis_title="",
yaxis_title="",
plot_bgcolor='#333',
paper_bgcolor='#333',
title_font=dict(size=30, color='#ffffff', family="Lato, sans-serif"),
font=dict(color='#ffffff', size=15),
legend=dict(orientation="h", yanchor="bottom", y=1.1, xanchor="center", x=0.5),
hoverlabel=dict(bgcolor='#333', font_size=13, font_family="Lato, sans-serif"))
fig_age.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig_age.show()