Get started quickly with Python Pandas, and learn how to process data like a cook!

pandas is a powerful python data processing library that excels in data analysis, cleaning and transformation Brilliant. Its flexible data structure and rich functions make it a powerful tool for data processing.

Data structure: DataFrame

DataFrame is the core data structure of Pandas, which is similar to a table and consists of rows and columns. Each row represents a data record, and each column represents an attribute of the record.

Data loading and reading

• Load from CSV file: pd.read_csv("filename.csv")
• Load from Excel file: pd.read_<strong class="keylink">excel</strong>("filename.xlsx")
• Load from JSON file: pd.read_<strong class="keylink">JSON</strong>("filename.<strong class="keylink">js</strong>on")

Data Cleaning

• Handling missing values: df.fillna(0)(Fill missing values ​​with 0)
• Remove duplicates: df.drop_duplicates()
• Type conversion: df["column"].astype(int) (Convert a column from object type to integer type)

Data conversion

• Merge DataFrame: pd.merge(df1, df2, on="column_name")
• Connect DataFrame: pd.concat([df1, df2], axis=1)(Connect by column)
• Group operation: df.groupby("column_name").agg({"column_name": "mean"}) (Group by column and calculate the average)

data analysis

• Descriptive statistics: df.describe() (calculate mean, median, standard deviation, etc.)
• Visualization: df.plot() (generate bar charts, line charts, etc.)
• Data aggregation: df.agg({"column_name": "sum"}) (calculate the sum of a column)

Advanced Features

• Conditional filtering: df[df["column_name"] > 10]
• Regular expression: df[df["column_name"].str.cont<strong class="keylink">ai</strong>ns("pattern")]
• Custom function: df["new_column"] = df["old_column"].apply(my_funct<strong class="keylink">io</strong>n)

Example

import pandas as pd

# Load data from CSV file
df = pd.read_csv("sales_data.csv")

# Clean data
df.fillna(0, inplace=True) # Fill in missing values

# Convert data
df["sale_date"] = pd.to_datetime(df["sale_date"]) # Convert date column to datetime type

# analyze data
print(df.describe()) # Display descriptive statistics

# Visualize data
df.plot(x="sale_date", y="sales") # Generate a line chart

# export data
df.to_csv("sales_data_processed.csv", index=False) # Export to CSV file

Conclusion

Pandas makes data processing a breeze, and its powerful features and flexible data structures make it a must-have tool for data scientists and analysts. By mastering the basics of Pandas, you can quickly and easily process and analyze complex data sets.

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