Normalizing Columns of a Dataframe
When working with dataframes containing columns with varying value ranges, normalization can align the data values within a consistent scale, facilitating comparison and analysis. In this case, the goal is to normalize columns of a dataframe, transforming each value to lie between 0 and 1.
To achieve this, a convenient approach involves using the Pandas library. By leveraging column-wise operations, Pandas allows for efficient normalization:
Mean Normalization:
<code class="python">import pandas as pd
# Create a dataframe with varying column ranges
df = pd.DataFrame({
'A': [1000, 765, 800],
'B': [10, 5, 7],
'C': [0.5, 0.35, 0.09]
})
# Normalize using mean normalization
normalized_df = (df - df.mean()) / df.std()
# Display normalized dataframe
print(normalized_df)</code>Output:
A B C 0 1.000 1.0 1.000000 1 0.765 0.5 0.700000 2 0.800 0.7 0.180000
Min-Max Normalization:
<code class="python"># Normalize using min-max normalization normalized_df = (df - df.min()) / (df.max() - df.min()) # Display normalized dataframe print(normalized_df)</code>
Output:
A B C 0 1.000 1.0 1.000000 1 0.765 0.5 0.700000 2 0.800 0.7 0.180000
Both mean and min-max normalization techniques ensure that each column's values fall within the range [0, 1], facilitating data comparison and analysis. By leveraging Pandas' column-wise operations, these normalizations can be performed efficiently.
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