Normalization puts values with wildly different units onto a common scale, so one giant number does not squash every other line into visual irrelevance. It preserves each series’s relative variation while making patterns easier to compare. section 6.11
Min–max normalization maps a feature to 0–1: subtract its minimum, then divide by its range. Zero represents that feature’s minimum and one its maximum. section 6.11
Plotting server trends
Clock speed in GHz, RAM in GB, and storage in GB share little numerical common ground. An unnormalized plot makes clock-speed growth look flat beside storage; normalizing reveals growth trends across all three. The trade-off: because every line starts at 0 and ends at 1, the chart cannot show which category grew faster overall. section 6.12 section 6.13
Listing 6.2 Visualizing a time series with normalization
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import MinMaxScaler
df_servers = pd.read_csv("AllServers.csv")
years = df_servers['Year'].values #1
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform\
(df_servers.drop(columns=['Year']))
plt.figure(figsize=(10, 6)) #2
for i, column_name in enumerate(df_servers.columns[1:]):
plt.plot(years, normalized_data[:, i], label=column_name) #3
plt.xlabel('Year') #4
plt.ylabel('Normalized Values') #4
plt.title('"All Servers" (Normalized) Specs Over Time') #4
plt.legend() #5
plt.grid(True)
plt.show() #6
This Python example uses sklearn’s MinMaxScaler on every column except Year, then plots each normalized series against time. section 6.13
A normalized chart can make a useful visual comparison, not a truth machine. Check the underlying data before drawing consequential conclusions, especially when the values came from an AI system. section 6.13
