Needing help Create custom parallel interactive bar chart
I need help with creating the middle chart The dashboard looks like butterfly chart for hires and exits, with a parallel bar chart in the middle showing headcount and promotions for each headcount grade level -as Power BI lacks this visual I am trying to do it with python but I am unable to find the right code to generate the middle graph and make it interactive with the bar charts on the left and right. ** so the requirement is creating three subplots: left for hires for each career level, middle for headcount/promotions for each career level *up of each other* in parallel and showing the (number, percentage ), and right for exits, all sharing the same y-axis (career levels).Python Evaluation
Can anybody with Python experience test my code and let's compare answers/notes, please? import pandas as pd import numpy as np import tensorflow as tf from tensorflow.keras import layers abalone_train = pd.read_csv( r"C:\Users\Anthony.DESKTOP-ES5HL78\Downloads\abalone.data", sep=“,”, names=[ “Sex”, “Length”, “Diameter”, “Height”, “Whole weight”, “Shucked weight”, “Viscera weight”, “Shell weight”, “Age” ] ) # Perform one-hot encoding for the ‘Sex’ column abalone_train = pd.get_dummies(abalone_train, columns=[‘Sex’]) abalone_features = abalone_train.copy() abalone_labels = abalone_features.pop(‘Age’) # Convert to numpy array and float32 data type abalone_features = abalone_features.astype(‘float32’) abalone_labels = abalone_labels.astype(‘float32’) # Create a sequential model model = tf.keras.Sequential([ layers.Dense(64, activation=‘relu’), layers.Dense(1) ]) # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.MeanSquaredError()) # Fit the model history = model.fit(abalone_features, abalone_labels, epochs=10, verbose=0) # Print history and model summary print(history.history) model.summary() { [74.98324584960938, 12.322690963745117, 7.765621185302734, 7.502653121948242, 7.229104518890381, 6.997042179107666, 6.799757957458496, 6.645227432250977, 6.521533489227295, 6.418800354003906]} Model: “sequential” --- # Layer (type) Output Shape Param # dense (Dense) (None, 64) 704 dense_1 (Dense) (None, 1) 65 ================================================================= Total params: 769 Trainable params: 769 Non-trainable params: 0 ## Data for this Question: [https://drive.google.com/file/d/1iSgWZUK…sp=sharing](https://drive.google.com/file/d/1iSgWZUK23Gw6R-2rUkKeEOl6BIwms8L8/view?usp=sharing)
