SAP General Ledger - Extract General Ledger Accounting Data to Power BI with Python Scripts
The Power BI Connector for SAP (PCS) from DVW Analytics can be used to extract data directly from SAP into Power BI Reports and Dashboards. In this blog series I will detail all the steps needed to extract data from SAP Tables and import the associated data into Power BI via Python Scripts generated in PCS where it can be easily refreshed.
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)
