-
Notifications
You must be signed in to change notification settings - Fork 0
/
company_visualization_hadler.py
193 lines (173 loc) · 6.46 KB
/
company_visualization_hadler.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
import urllib.parse
import matplotlib.pyplot as plt
import pandas as pd
from streamlit_echarts import st_echarts
import streamlit as st
def bond_purchase_heatmap(company_ov_vi, companies):
Years = companies['Year'].drop_duplicates().sort_values().tolist()
months = companies['Month'].drop_duplicates().sort_values().tolist()
print(Years)
print(months)
transaction_counts = companies.groupby(['Year', 'Month']).size().reset_index(name='transactions')
# Pivot to get a matrix format suitable for a heatmap
transaction_matrix = transaction_counts.pivot_table(values='transactions', index='Year', columns='Month', fill_value=0)
# Ensure the months are in the correct order
months_ordered = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
transaction_matrix = transaction_matrix.reindex(columns=months_ordered)
transaction_matrix.fillna(0, inplace=True)
data_for_heatmap = [
[month, str(year), int(transaction_matrix.loc[year, month])]
for year in transaction_matrix.index
for month in transaction_matrix.columns
]
# data_for_heatmap = [[d[1], d[0], d[2] if d[2] != 0 else "-"] for d in data_for_heatmap]
data_for_heatmap = [
[str(year), str(month), transaction_count]
for year, month, transaction_count in data_for_heatmap
if transaction_count != 0 # Exclude zero counts
]
max_count = max(count for _, _, count in data_for_heatmap if count is not None)
option = {
"tooltip": {"position": "top"},
"grid": {"height": "50%", "top": "10%"},
"xAxis": {"type": "category", "data": months_ordered, "splitArea": {"show": True}},
"yAxis": {"type": "category", "data": Years, "splitArea": {"show": True}},
"visualMap": {
"min": 1,
"max": max_count,
"calculable": True,
"orient": "horizontal",
"left": "center",
"bottom": "15%",
},
"series": [
{
"name": "Bonds Purchased",
"type": "heatmap",
"data": data_for_heatmap,
"label": {"show": True},
"emphasis": {
"itemStyle": {"shadowBlur": max_count, "shadowColor": "rgba(0, 0, 0, 0.5)"}
},
}
],
}
company_ov_vi.header("Annual Trends in Corporate Electoral Bond Investments")
with company_ov_vi:
st_echarts(option, height="500px")
def top_category(company_ov_vi, category_group, n, left_Col):
top_5_df = category_group.head(n)
top_5_df["Amount (₹ Cr)"] = top_5_df["Amount (₹ Cr)"].str.replace(",", "").astype(float)
data = (
top_5_df[["Category", "Amount (₹ Cr)"]]
.rename(columns={"Category": "name", "Amount (₹ Cr)": "value"})
.to_dict("records")
)
print(data)
category_options = {
"tooltip": {"trigger": "item"},
"legend": {"orient": "vertical", "left": "left"},
"dataset": [
{
"source": data,
}
],
"series": [
{
"type": "pie",
"radius": "50%",
},
{
"type": "pie",
"radius": "50%",
"label": {
"position": "inside",
"formatter": "{d}%",
"color": "black",
"fontSize": 18,
},
"emphasis": {
"label": {"show": "true"},
"itemStyle": {
"shadowBlur": 10,
"shadowOffsetX": 0,
"shadowColor": "rgba(0, 0, 0, 0.5)",
},
},
},
],
}
left_Col.header(f"Top {n} Contribution Categories: Distribution of Shares")
with company_ov_vi:
with left_Col:
st_echarts(
options=category_options,
height="600px",
)
def top_contributors(company_ov_vi, sorted_company, n, right_col):
top_5_df = sorted_company.head(n)
others = pd.DataFrame(
data={"Company": ["Other"], "Amount": [sorted_company["Amount"][n:].sum()]}
)
others["Amount (₹ Cr)"] = (others["Amount"] / 10**7).map("{:,.2f}".format)
combined_df = pd.concat([top_5_df, others])
combined_df["Amount (₹ Cr)"] = (
combined_df["Amount (₹ Cr)"].str.replace(",", "").astype(float)
)
data = (
combined_df[["Company", "Amount (₹ Cr)"]]
.rename(columns={"Company": "name", "Amount (₹ Cr)": "value"})
.to_dict("records")
)
options = {
"tooltip": {"trigger": "item"},
"legend": {"orient": "horizontal ", "bottom": "bottom"},
"dataset": [
{
"source": data,
}
],
"series": [
{
"type": "pie",
"radius": "50%",
},
{
"type": "pie",
"radius": "50%",
"label": {
"position": "inside",
"formatter": "{d}%",
"color": "black",
"fontSize": 18,
},
"emphasis": {
"label": {"show": "true"},
"itemStyle": {
"shadowBlur": 10,
"shadowOffsetX": 0,
"shadowColor": "rgba(0, 0, 0, 0.5)",
},
},
},
],
}
right_col.header(f"Top {n} Electoral Contributors' Share in Total Contributions")
with company_ov_vi:
with right_col:
st_echarts(
options=options,
height="600px",
)
def display_overall_company_visualization(
sorted_company, parent_company_group, category_group, companies, company_ov_vi
):
n = company_ov_vi.selectbox(
"Select Number of Entries to Display", [5,10,15,20]
)
right_col, left_Col = company_ov_vi.columns([3,3])
right_col.markdown(" <style>iframe{ height: 500px !important } ", unsafe_allow_html=True)
left_Col.markdown(" <style>iframe{ height: 500px !important } ", unsafe_allow_html=True)
top_contributors(company_ov_vi, sorted_company, n, right_col)
top_category(company_ov_vi, category_group, n, left_Col)
bond_purchase_heatmap(company_ov_vi, companies)