


Python Pandas practical drill, a quick advancement for data processing novices!
- Use
read_csv()
to read the CSV file:df = pd.read_csv("data.csv")
- Handling missing values:
- Remove missing values:
df = df.dropna()
- Fill missing values:
df["column_name"].fillna(value)
- Remove missing values:
- Convert data type:
df["column_name"] = df["column_name"].astype(dtype)
-
Sort and group by:
- Sort:
df.sort_values(by="column_name")
- Group:
groupby_object = df.groupby(by="column_name")
- Sort:
2. Data analysis
- statistics
-
describe()
: View basic statistics of data -
mean()
: Calculate the average value -
std()
: Calculate standard deviation
-
- Draw a chart:
-
plot()
: Generate various chart types, such as line charts and scatter charts -
bar()
:Generate bar chart -
pie()
:Generate pie chart
-
- Data aggregation:
-
agg()
: Apply aggregate function on grouped data -
pivot_table()
: Create a crosstab for summarizing and analyzing data
-
3. Data operation
-
Indices and slices:
-
loc[index_values]
: Get data by index value -
iloc[index_values]
: Get data by index position -
query()
: Filter data by conditions
-
- Data operations:
-
append()
:Append data to DataFrame -
merge()
: Merge two or more DataFrames -
concat()
: Concatenate multiple DataFrames together
-
- Data conversion:
-
apply()
:Apply the function row by row or column by column -
lambda()
: Create an anonymous function to transform data
-
4. Advanced skills
- Custom functions: Create and use custom functions to extend the functionality of pandas
- Vectorization operations: Use NumPy’s vectorization functions to improve efficiency
- 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
- Analyze customer data: Understand customer behavior, purchasing patterns and trends
- Processing financial data: calculating financial indicators, analyzing stock performance
- Exploring scientific data: processing sensor data and analyzing experimental results
The above is the detailed content of Python Pandas practical drill, a quick advancement for data processing novices!. For more information, please follow other related articles on the PHP Chinese website!

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SUM in Oracle is used to calculate the sum of non-null values, while COUNT counts the number of non-null values of all data types, including duplicate values.

The SUM() function in SQL is used to calculate the sum of numeric columns. It can calculate sums based on specified columns, filters, aliases, grouping and aggregation of multiple columns, but only handles numeric values and ignores NULL values.

MySQL's AVG() function is used to calculate the average of numeric values. It supports multiple usages, including: Calculate the average quantity of all sold products: SELECT AVG(quantity_sold) FROM sales; Calculate the average price: AVG(price); Calculate the average sales volume: AVG(quantity_sold * price). The AVG() function ignores NULL values, use IFNULL() to calculate the average of non-null values.

The COUNT function in Oracle is used to count non-null values in a specified column or expression. The syntax is COUNT(DISTINCT <column_name>) or COUNT(*), which counts the number of unique values and all non-null values respectively.

GROUP BY is an aggregate function in SQL that is used to group data based on specified columns and perform aggregation operations. It allows users to: Group data rows based on specific column values. Apply an aggregate function (such as sum, count, average) to each group. Create meaningful summaries from large data sets, perform data aggregation and grouping.

SC stands for SELECT COUNT in SQL, an aggregate function used to count the number of records whether or not a condition is met. SC syntax: SELECT COUNT(*) AS record_count FROM table_name WHERE condition, where COUNT(*) counts the number of all records, table_name is the table name, and condition is an optional condition (used to count the number of records that meet the condition).

The SQL SUM function calculates the sum of a set of numbers by adding them together. The operation process includes: 1. Identifying the input value; 2. Looping the input value and converting it into a number; 3. Adding each number to accumulate a sum; 4. Returning the sum result.

The HAVING clause is used to filter the result set grouped by the GROUP BY clause. Its syntax is HAVING <condition>, where <condition> is a Boolean expression. The difference with the WHERE clause is that the HAVING clause filters groups after aggregation, while the WHERE clause filters rows before aggregation. It can be used to filter grouped result sets, perform aggregate calculations on data, create hierarchical reports, or summarize queries.
