This project is created to enable Python users to easily generate personalized tables with custom subtotals
The project is designed for creating tables with specified styling using Plotly. It includes methods to generate individual components such as title, data, and subtotals tables, and a comprehensive method create_table_with_title that combines these components into a vertically stacked table with customizable title, colors, and data. The class allows for easy integration of tables into Plotly figures with specified heights and styling details.
Install the Plotly module in Python to seamlessly utilize the features of this project for creating customized tables.
This code is compatible with both Python and Jupyter environments. To get started, include the Plotly_Table class provided below in your script.
from plotly.subplots import make_subplots
import plotly.graph_objects as go
class Plotly_Table:
def __init__(self):
# ... (rest of the class code)First Instantiate the class by creating an instance:
plotly_table = Plotly_Table() # Define the instance
# ... (rest of the code)To utilize this functionality, prepare two dataframes. The first dataframe should contain your data, while the second one should consist of the corresponding subtotals. Ensure both dataframes have identical columns. With a separate dataframe for subtotals, you have the flexibility to calculate subtotal values using any desired methodology.
# Code for Section 1
from plotly.subplots import make_subplots
import plotly.graph_objects as go
class Plotly_Table:
def __init__(self):
# Table Colors
self.header_fill_color = "#0071BC" # Blue fill color in header and titles
self.header_font_color = 'white'
self.data_fill_color = 'white'
self.line_color = '#D4D2CB' # Gray table line color
# Heights
self.title_height = 30
self.header_height=30
self.rows_height=25
self.subtotals_height =25
def title_table(self, title_text="Insert Title"):
return go.Table(
header=dict(values=[""], height=0),
cells=dict(
values=[f"<b>{title_text}</b>"],
fill_color=self.header_fill_color,
line_color=self.line_color,
font=dict(color=self.header_font_color),
height=self.title_height))
def data_table(self, df):
self.data_height = self.rows_height * len(df) + self.header_height
return go.Table(
header=dict(
values=["<b>" + value + "</b>" for value in df.columns],
line_color=self.line_color,
fill_color=self.header_fill_color,
font=dict(color=self.header_font_color),
height=self.header_height),
cells=dict(
values=[df[col] for col in df.columns],
fill_color=self.data_fill_color,
line_color=self.line_color,
height=self.rows_height))
def subtotals_table(self, df):
df.iloc[-1, :] = df.iloc[-1, :].values.astype(str) #We make values as string to bold
return go.Table(
header=dict(values=[""], height=0),
cells=dict(
values=["<b>" + df[col] + "</b>" for col in df.columns],
fill_color=self.data_fill_color,
line_color=self.line_color,
height=self.subtotals_height))
def create_table_with_title(self, data_df , subtotals_df, title_text='Insert Title',color_fill="#0071BC"):
self.header_fill_color = color_fill
title_table=self.title_table(title_text=title_text)
data_table = self.data_table(df=df)
subtotals_df = self.subtotals_table(df=subtotals_df)
# Heights:
total_height = (self.data_height + self.subtotals_height + self.title_height)+10
percentages = [self.title_height/total_height, self.data_height/total_height, (self.subtotals_height+20)/total_height]
specs = [[{'type': 'table'}]] * 3
figure = make_subplots(rows=3, cols=1, specs=specs, row_heights=percentages, vertical_spacing=0.001)
figure.add_trace(title_table, row=1, col=1)
figure.add_trace(data_table, row=2, col=1)
figure.add_trace(subtotals_df, row=3, col=1)
figure.update_layout(
autosize=False,
height=total_height+75,
margin=dict(t=20, b=20))
return figureimport pandas as pd
import random
n = 15 #Change to any number and still works
data = {
'Student': [f'Student_{i}' for i in range(1, n + 1)],
'Corporate Finance': [round(random.uniform(6.0, 10.0), 2) for _ in range(n)],
'Capital Markets': [round(random.uniform(6.5, 9.0), 2) for _ in range(n)],
'FRA': [round(random.uniform(7.0, 8.5), 2) for _ in range(n)]
}
df = pd.DataFrame(data)
# Generating df with subtotals
column_means = df.select_dtypes(include='number').mean()
subtotals_df = pd.DataFrame([column_means.round(2)], columns=column_means.index)
subtotals_df.insert(0, 'Student', "Total Average")
# Generating the table
plotly_table = Plotly_Table() # First defining the instance
fig1 = plotly_table.create_table_with_title(data_df=df,
subtotals_df=subtotals_df,
title_text="Financial Course Performance Summary",
color_fill="#005c22")
fig1.show()
import pandas as pd
import random
import numpy as np
n_clients = 18
data_accounts = {
'Client': [f'Client_{i}' for i in range(1, n_clientes + 1)],
'Market Value': [round(random.uniform(10000, 50000), 2) for _ in range(n_clients)],
'% in Stocks and Options': [round(random.uniform(0, 100), 2) for _ in range(n_clients)],
}
df = pd.DataFrame(data_accounts)
df.insert(3, '% in Cash', (100 -df['% in Stocks and Options']).round(2))
# # Generating df with subtotals (Personalized)
subtotals_df = pd.DataFrame(columns=df.columns)
# Calculate the weighted average for '% in Cash' adn '% in stocks and options'
weighted_avg_cash = np.average(df['% in Cash'], weights=df['Market Value']).round(2)
subtotals_df.loc[0] = [np.nan, df['Market Value'].sum(), 100-weighted_avg_cash, weighted_avg_cash]
subtotals_df['Client'].loc[0] = "Total"
subtotals_df
# Generating the table
plotly_table = Plotly_Table() # First defining the instance
fig2 = plotly_table.create_table_with_title(data_df=df,
subtotals_df=subtotals_df,
title_text="Financial Course Performance Summary",
color_fill="#000b5c")
fig2.show()