Essential Insights on Using Pandas in Python
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Chapter 1: Key Techniques in Pandas
In this chapter, we will explore several essential methods in Pandas that can significantly streamline your data manipulation tasks.
Here's a noteworthy example of applying a function across a DataFrame.
Section 1.1: Utilizing .apply
To demonstrate, let’s create a simple DataFrame:
import pandas as pd
df = pd.DataFrame([
['apple', 4],
['orange', 5],
['pear', 6]
], columns=['fruit', 'price'])
Now, if we aim to square all values in the price column, we can achieve this by using the .apply method, which allows us to apply a function to each element in the specified column.
df['price'] = df['price'].apply(lambda x: x**2)
Subsection 1.1.1: Creating a New Column
Continuing with our DataFrame example, let's create a new column called price_squared to hold the squared values of the price column:
df['price_squared'] = df['price'] ** 2
Section 1.2: Renaming Columns
Next, if we want to rename the columns in our DataFrame, we can utilize the .rename method:
df = df.rename(columns={'fruit':'plant_flesh', 'price':'monetary_value'})
Chapter 2: Data Filtering Techniques
The first video titled "9 Things I Wish I Knew Earlier About Pandas" provides valuable insights into these foundational techniques.
Section 2.1: Filtering DataFrames
Let’s explore how to filter our DataFrame to include only rows where the price is less than or equal to 5:
df[df['price'] <= 5]
Subsection 2.1.1: Handling Missing Values
In scenarios where our DataFrame includes missing values (NaNs), we can filter them out as follows:
df[df['price'].isna()]
To exclude NaN values, we can invert the condition:
df[~df['price'].isna()]
Section 2.2: Grouping Data
To calculate average prices per category, we can group our DataFrame by shop and use the .mean method:
df.groupby('shop').mean()
Alternatively, we can apply other aggregation methods like .sum:
df.groupby('shop').sum()
Chapter 3: Advanced Data Manipulation
The second video "6 Money Habits I Wish I Had Learned Earlier" explores additional best practices that can benefit your data analysis.
Section 3.1: Counting Unique Values
Utilizing the .value_counts() method allows us to determine the frequency of values in a column:
df['shop'].value_counts()
df['price'].value_counts()
Subsection 3.1.1: Filling Missing Values
If we prefer to fill NaN values rather than discard them, we can use:
df['price'] = df['price'].fillna(100)
To use the average of existing values instead:
average = df['price'].mean()
df['price'] = df['price'].fillna(average)
Section 3.2: Iterating Through Groups
Finally, to perform operations on each group within our DataFrame, we can iterate through the grouped object:
for key, group in df.groupby('shop'):
print('key =', key)
display(group)
Conclusion
I hope this overview has clarified some essential techniques for working with Pandas. If you appreciate this content, please consider supporting my work by leaving a comment or sharing your favorite insights! Your engagement means a lot to me!