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Data Visualization
Matplotlib and Seaborn libraries enable informative chart creation:
import seaborn as sns
import matplotlib.pyplot as plt
sns.histplot(df['column'])
plt.show()
Exploratory Data Analysis
Use basic statistical methods to understand your data:
df.describe()
df.corr()
Machine Learning with Scikit-learn
Implement predictive models effortlessly:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression().fit(X_train, y_train)
Conclusion
Mastering Python for data analysis opens infinite possibilities in the data science field. Continue practicing with real projects to solidify your knowledge.
Originally published in Spanish at mgobeaalcoba.github.io/blog/python-data-analysis-guide/