Python Pandas practical drill, a quick advancement for data processing novices!

1. Use read_csv() to read the CSV file: df = pd.read_csv("data.csv")
2. Handling missing values:
   • Remove missing values: df = df.dropna()
   • Fill missing values: df["column_name"].fillna(value)
3. Convert data type: df["column_name"] = df["column_name"].astype(dtype)
4. Sort and group by:
   • Sort: df.sort_values(by="column_name")
   • Group: groupby_object = df.groupby(by="column_name")

2. Data analysis

1. statistics
   • describe(): View basic statistics of data
   • mean(): Calculate the average value
   • std(): Calculate standard deviation
2. Draw a chart:
   • plot(): Generate various chart types, such as line charts and scatter charts
   • bar()：Generate bar chart
   • pie()：Generate pie chart
3. Data aggregation:
   • agg(): Apply aggregate function on grouped data
   • pivot_table(): Create a crosstab for summarizing and analyzing data

3. Data operation

1. Indices and slices:
   • loc[index_values]: Get data by index value
   • iloc[index_values]: Get data by index position
   • query(): Filter data by conditions
2. Data operations:
   • append()：Append data to DataFrame
   • merge(): Merge two or more DataFrames
   • concat(): Concatenate multiple DataFrames together
3. Data conversion:
   • apply()：Apply the function row by row or column by column
   • lambda(): Create an anonymous function to transform data

4. Advanced skills

1. Custom functions: Create and use custom functions to extend the functionality of pandas
2. Vectorization operations: Use NumPy’s vectorization functions to improve efficiency
3. Data cleaning:
   • str.strip(): Remove whitespace characters from string
   • str.replace(): Replace characters in the string or regular expression
   • str.lower(): Convert the string to lowercase

5. Case application

1. Analyze customer data: Understand customer behavior, purchasing patterns and trends
2. Processing financial data: calculating financial indicators, analyzing stock performance
3. Exploring scientific data: processing sensor data and analyzing experimental results

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