Lære Essential Python Tools for Data Cleaning | Foundations of Data Cleaning

When you begin cleaning data in Python, two essential libraries stand out: pandas and numpy. These libraries are widely used because they make it simple and efficient to load, inspect, and transform data. pandas is designed for working with structured data, such as tables and spreadsheets, using its powerful DataFrame and Series objects. With pandas, you can easily filter, sort, aggregate, and reshape your data. numpy focuses on numerical operations and provides fast, flexible tools for working with arrays of numbers. Combining pandas and numpy gives you a strong foundation for handling missing values, correcting data types, and performing calculations that are common in real-world data cleaning tasks.


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import pandas as pd
import numpy as np

# Create a simple pandas DataFrame
data = {
    "name": ["Alice", "Bob", "Charlie", "David", np.nan],
    "age": [25, 30, np.nan, 22, 28],
    "score": [88.5, 92.0, 85.0, np.nan, 90.0]
}
df = pd.DataFrame(data)

# Inspect the DataFrame
print("DataFrame head:")
print(df.head())

# Check for missing values
print("\nMissing values in each column:")
print(df.isnull().sum())

# Fill missing ages with the mean age using numpy
mean_age = np.nanmean(df["age"])
df["age"] = df["age"].fillna(mean_age)
print("\nDataFrame after filling missing ages with the mean:")
print(df)

# Convert all names to lowercase using pandas string methods
df["name"] = df["name"].str.lower()
print("\nDataFrame after standardizing names to lowercase:")
print(df)

You can see how pandas makes it easy to inspect your data, check for missing values, and apply transformations. numpy is often used alongside pandas to perform numerical calculations, such as finding the mean of a column while ignoring missing values. By combining these libraries, you can quickly prepare your data for further analysis or modeling.

1. Which pandas function is commonly used to check for missing values in a DataFrame?

2. What is the main advantage of using numpy with pandas for data cleaning?

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              1234567891011121314151617181920212223242526272829
            
import pandas as pd
import numpy as np

# Create a simple pandas DataFrame
data = {
    "name": ["Alice", "Bob", "Charlie", "David", np.nan],
    "age": [25, 30, np.nan, 22, 28],
    "score": [88.5, 92.0, 85.0, np.nan, 90.0]
}
df = pd.DataFrame(data)

# Inspect the DataFrame
print("DataFrame head:")
print(df.head())

# Check for missing values
print("\nMissing values in each column:")
print(df.isnull().sum())

# Fill missing ages with the mean age using numpy
mean_age = np.nanmean(df["age"])
df["age"] = df["age"].fillna(mean_age)
print("\nDataFrame after filling missing ages with the mean:")
print(df)

# Convert all names to lowercase using pandas string methods
df["name"] = df["name"].str.lower()
print("\nDataFrame after standardizing names to lowercase:")
print(df)

1. Which pandas function is commonly used to check for missing values in a DataFrame?

2. What is the main advantage of using numpy with pandas for data cleaning?

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 2