How to use open source datasets in Python?

With the advent of the big data era, data analysis and machine learning have become popular fields. However, how to obtain the data set, analyze it and train the model can be a difficult task for beginners. To solve this problem, the open source community has provided rich data sets, and Python, as a popular programming language, also provides various methods to use these data sets.

This article introduces methods and tools for using open source data sets in Python, such as data loading, browsing, cleaning, visualization and analysis. We will use publicly available data sets for practical demonstrations to help readers master these skills.

1. Loading the data set

First, we need to load the data set into the Python program. There are many open source datasets that can be downloaded from the web, such as UCI Machine Learning Repository, Kaggle, etc. These data sets are generally saved in various formats such as CSV, JSON, and XML.

In Python, pandas is a very useful library. We can use pandas to load a CSV format data set with a few lines of code:

import pandas as pd data = pd.read_csv("example.csv")

2. Data browsing

Once the data set is loaded into Python, we can start exploring the data. We can use the head() method of pandas to view the first few rows of data:

print(data.head())

If we want to view the last few rows in the data set, we can use the tail() method.

We can also use the shape attribute to get the size of the data set:

print(data.shape)

In addition, we can use the describe() method to get simple statistics of the data set, such as the minimum value and maximum value , average, etc.:

print(data.describe())

3. Data Cleaning

When we browse the data set, we may find that there are missing values, outliers, or duplicate values in the data set. question. In data analysis and machine learning, these problems are very serious, so we need to clean them.

For missing values, we can use the fillna() method to fill them with 0 or the average value:

data.fillna(0, inplace=True)

If we want to delete duplicate rows in the data set, we can use drop_duplicates( ) Method:

data.drop_duplicates(inplace=True)

For outliers, we can use the standard deviation to determine whether it is abnormal and replace it with the mean:

mean = data["col"].mean() std = data["col"].std() cut_off = std * 3 lower, upper = mean - cut_off, mean + cut_off new_data = [x if x > lower and x < upper else mean for x in data["col"]] data["col"] = new_data

4. Data Visualization

Data visualization is one of the important steps in data analysis. In Python, we can use libraries such as Matplotlib and Seaborn for data visualization.

For example, we can use the Matplotlib library to draw a line chart in the data set:

import matplotlib.pyplot as plt plt.plot(data["col"]) plt.show()

or use the Pairplot method of the Seaborn library to make a distribution chart of multiple variables:

import seaborn as sns sns.pairplot(data)

5. Data Analysis

After data visualization, we can conduct more in-depth data analysis, such as building models, training models, predictions, etc. Python provides many libraries to support these operations, such as Scikit-learn and TensorFlow, among others.

For example, we can use the Scikit-learn library to build a linear regression model:

from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split X = data[["col1", "col2"]] y = data["target_col"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = LinearRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test)

In the above example, we use the train_test_split method to divide the data set into a training set and a test set, and then Use the LinearRegression class to build a model, and finally use the predict method to predict the test set.

Conclusion

This article introduces how to use open source datasets for data analysis and machine learning in Python. We use the pandas library to load and browse datasets, the Matplotlib and Seaborn libraries for data visualization, and the Scikit-learn library to build and train models. These techniques and tools are not only applicable to the open source data sets mentioned in this article, but also to other types of data sets, such as Web data, sensor data, etc. As data analysis and machine learning develop, these technologies and tools will continue to be updated and improved, providing better performance and ease of use.

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