IT

글

Using venv in Python

1월 04, 2025

Using venv in Python allows you to create a virtual environment, which is an isolated environment to manage dependencies for a specific project. Here's how you can use it step by step: 1. Create a Virtual Environment Open your terminal or command prompt. Navigate to the folder where you want to create the virtual environment. Run the following command: python -m venv myenv Replace myenv with the name you want for your virtual environment. This will create a directory named myenv containing the virtual environment. 2. Activate the Virtual Environment Activation depends on your operating system: On Windows : myenv \S cripts \a ctivate On macOS/Linux : source myenv /bin/ activate After activation, your terminal prompt will change, showing the name of the virtual environment (e.g., (myenv) ). 3. Install Dependencies With the virtual environment activated, you can now install packages using pip : pip install package _n ame For example: pip install req...

자세한 내용 보기

'Series' object has no attribute 'reshape'

1월 04, 2025

The error 'Series' object has no attribute 'reshape' occurs because pandas.Series objects do not have a reshape method, as reshape is a method of NumPy arrays. If you're trying to reshape data, you need to convert the Series to a NumPy array first. Here's how to address this issue: Solution: Use to_numpy() to Convert to a NumPy Array Convert the Series to a NumPy array, then reshape it. import pandas as pd # Example Series s = pd.Series([ 1 , 2 , 3 , 4 , 5 , 6 ]) # Convert to NumPy array and reshape reshaped_array = s.to_numpy().reshape( 2 , 3 ) print(reshaped_array) Explanation to_numpy() : Converts the Series into a NumPy array. reshape() : Applies to NumPy arrays and allows you to specify the new dimensions. Common Use Case Example You might encounter this issue when working with data transformations, like reshaping for machine learning models: # Example: Converting a Series into a 2D array s = pd.Series([1, 2, 3, 4, 5, 6]) # Reshape t...

자세한 내용 보기

To get a row from a pandas.core.frame.DataFrame in Python

1월 04, 2025

To get a row from a pandas.core.frame.DataFrame in Python, you can use several methods depending on how you want to access the row. Here's how you can do it: 1. Access by Index Use iloc or loc : iloc : Access by integer-based position (row number). loc : Access by label-based index. import pandas as pd # Sample DataFrame data = {' A ': [1, 2, 3], ' B ': [4, 5, 6], ' C ': [7, 8, 9]} df = pd. DataFrame ( data ) # Access the first row (integer index) row1 = df.iloc[ 0 ] print (row1) # Access a row by index label (if set) row_by_label = df.loc[ 0 ] # Assuming index is not customized print (row_by_label) 2. Access Rows Based on Conditions You can filter rows based on a condition: # Get rows where column 'A' equals 2 filtered_row = df[df[ 'A' ] == 2] print (filtered_row) 3. Access Rows Using Slicing For multiple rows, use slicing with iloc or loc : # Get the first two rows rows = df.iloc[: 2 ] print ( rows ) # Ge...

자세한 내용 보기

ObjectMapper In the Jackson library

1월 03, 2025

In the Jackson library, you can use ObjectMapper to deserialize a JSON string into a Java object. The ObjectMapper.readValue method is commonly used for this purpose. Here's how you can do it: Example JSON String: { "name" : "Alice" , "age" : 25 } Java Class: public class Person { private String name; private int age; // Getters and setters public String getName () { return name; } public void setName (String name) { this .name = name; } public int getAge () { return age; } public void setAge ( int age) { this .age = age; } @Override public String toString () { return "Person{name='" + name + "', age=" + age + '}' ; } } Code to Deserialize: import com.fasterxml.jackson.databind.ObjectMapper; public class JsonToObjectExample { public static void main( String [] args) ...

자세한 내용 보기

Jackson library

1월 03, 2025

In the Jackson library, the JsonNode class represents a node in a JSON tree. To extract a list (e.g., an array) from a JsonNode , you can use methods like get , elements() , or findValues . Here's how you can achieve this: Example: Extracting a List from JsonNode Given JSON: { "names" : [ "Alice" , "Bob" , "Charlie" ] } Java Code: import com.fasterxml.jackson.databind.JsonNode; import com.fasterxml.jackson.databind.ObjectMapper; import java.util.ArrayList; import java.util.Iterator; import java.util.List; public class JsonNodeExample { public static void main(String[] args) throws Exception { String jsonString = "{ \"names\": [\"Alice\", \"Bob\", \"Charlie\"] }" ; // Parse JSON ObjectMapper mapper = new ObjectMapper(); JsonNode rootNode = mapper.readTree(jsonString); // Extract the "names" array as a JsonNode ...

자세한 내용 보기

Creating an synchronous HTTP request using Spring RestTemplate

1월 03, 2025

While RestTemplate is now in maintenance mode and WebClient is recommended for new applications, it’s still widely used in existing projects. Here’s how you can configure and use RestTemplate in a Spring Boot application: 1. Add Dependencies Ensure the following dependency is present in your pom.xml : < dependency > < groupId > org.springframework.boot </ groupId > < artifactId > spring-boot-starter-web </ artifactId > </ dependency > 2. Configure a RestTemplate Bean Define a RestTemplate bean in a configuration class to enable dependency injection: import org.springframework.boot.web.client.RestTemplateBuilder; import org.springframework.context. annotation .Bean; import org.springframework.context. annotation .Configuration; import org.springframework.web.client.RestTemplate; @Configuration public class RestTemplateConfig { @Bean public RestTemplate restTemplate(RestTemplateBuilder builder) { ret...

자세한 내용 보기