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By Kevin McAleer, 2 Minutes
This lesson delves into Data Analysis and Aggregation with Pandas. Effective data analysis often involves summarizing data, grouping it based on certain criteria, and performing aggregate computations. We will explore these powerful capabilities in Pandas to derive meaningful insights from data.
Pandas provides convenient methods to get descriptive statistics:
# Descriptive statistics summary = df.describe()
The describe() method returns a Data Frame with descriptive statistics for each column in the original Data Frame, like this:
describe()
Column1 Column2 count 5.000000 5.000000 mean 0.000000 0.000000 std 0.707107 0.707107 min -1.000000 -1.000000 25% -1.000000 -1.000000 50% 0.000000 0.000000 75% 1.000000 1.000000 max 1.000000 1.000000
You can also explore unique values and counts:
# Unique values and counts unique_values = df['ColumnName'].unique() value_counts = df['ColumnName'].value_counts()
Group data by one or more columns:
# Grouping data grouped = df.groupby('ColumnName')
Perform aggregate computations on groups:
# Aggregating data aggregated_data = grouped.aggregate(np.sum)
Create pivot tables for multidimensional data analysis:
# Pivot tables pivot_table = df.pivot_table(values='ValueColumn', index='RowColumn', columns='ColumnColumn')
In this lesson, we’ve explored essential aspects of data analysis and aggregation using Pandas. Understanding how to summarize, group, and aggregate data is crucial for effective data analysis and gaining insights.
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