Odoo10 xpath inherit field on purchase order line

I'm trying to code on module.

I'm trying to code on module.

but i'm stuck. it doesn't work.

Python code

class PurchaseCurrency(models.Model):
    _inherit = "purchase.order.line"
new_currency = fields.Float(string="Test")

my xml

<record id="new_currency_line" model="ir.ui.view">
<field name="name">purchase.order.form</field>
<field name="model">purchase.order</field>
<field name="inherit_id" ref="purchase.purchase_order_form"/>
<field name="arch" type="xml">
<xpath expr="//field[@name='order_line']/tree/field[@name='product_qty']" position="after">
<field name="new_currency"/>
</xpath>
</field>
</record>

and error warning

ParseError: "Error while validating constraint

Field new_currency does not exist

Error context:

anyone can help me?

Python XML Parser Tutorial: How use XML Minidom class

Python XML Parser Tutorial: How use XML Minidom class

Python enables you to parse and modify XML document. In order to parse XML document you need to have the entire XML document in memory. In this tutorial, we will see how we can use XML minidom class in Python to load and parse XML file.

What is XML?

XML stands for eXtensible Markup Language. It was designed to store and transport small to medium amounts of data and is widely used for sharing structured information.

Python enables you to parse and modify XML document. In order to parse XML document you need to have the entire XML document in memory. In this tutorial, we will see how we can use XML minidom class in Python to load and parse XML file.

In this tutorial, we will learn:

  • How to Parse XML using minidom
  • How to Create XML Node
  • How to Parse XML using ElementTree
How to Parse XML using minidom

We have created a sample XML file that we are going to parse.

Step 1) Inside file, we can see first name, last name, home and the area of expertise (SQL, Python, Testing and Business)

Step 2) Once we have parsed the document, we will print out the "node name" of the root of the document and the "firstchild tagname". Tagname and nodename are the standard properties of the XML file.

  • Import the xml.dom.minidom module and declare file that has to be parsed (myxml.xml)
  • This file carries some basic information about employee like first name, last name, home, expertise, etc.
  • We use the parse function on the XML minidom to load and parse the XML file
  • We have variable doc and doc gets the result of the parse function
  • We want to print the nodename and child tagname from the file, so we declare it in print function
  • Run the code- It prints out the nodename (#document) from the XML file and the first child tagname (employee) from the XML file

Note:

Nodename and child tagname are the standard names or properties of an XML dom. In case if you are not familiar with these type of naming conventions.

Step 3) We can also call the list of XML tags from the XML document and printed out. Here we printed out the set of skills like SQL, Python, Testing and Business.

  • Declare the variable expertise, from which we going to extract all the expertise name employee is having
  • Use the dom standard function called "getElementsByTagName"
  • This will get all the elements named skill
  • Declare loop over each one of the skill tags
  • Run the code- It will give list of four skills
How to Create XML Node

We can create a new attribute by using "createElement" function and then append this new attribute or tag to the existing XML tags. We added a new tag "BigData" in our XML file.

  1. You have to code to add the new attribute (BigData) to the existing XML tag
  2. Then you have to print out the XML tag with new attributes appended with existing XML tag

  • To add a new XML and add it to the document, we use code "doc.create elements"
  • This code will create a new skill tag for our new attribute "Big-data"
  • Add this skill tag into the document first child (employee)
  • Run the code- the new tag "big data" will appear with the other list of expertise

XML Parser Example

Python 2 Example

import xml.dom.minidom

def main():
# use the parse() function to load and parse an XML file
   doc = xml.dom.minidom.parse("Myxml.xml");
  
# print out the document node and the name of the first child tag
   print doc.nodeName
   print doc.firstChild.tagName
  
# get a list of XML tags from the document and print each one
   expertise = doc.getElementsByTagName("expertise")
   print "%d expertise:" % expertise.length
   for skill in expertise:
     print skill.getAttribute("name")
    
# create a new XML tag and add it into the document
   newexpertise = doc.createElement("expertise")
   newexpertise.setAttribute("name", "BigData")
   doc.firstChild.appendChild(newexpertise)
   print " "

   expertise = doc.getElementsByTagName("expertise")
   print "%d expertise:" % expertise.length
   for skill in expertise:
     print skill.getAttribute("name")
    
if name == "__main__":
  main();

Python 3 Example

import xml.dom.minidom

def main():
    # use the parse() function to load and parse an XML file
    doc = xml.dom.minidom.parse("Myxml.xml");

    # print out the document node and the name of the first child tag
    print (doc.nodeName)
    print (doc.firstChild.tagName)
    # get a list of XML tags from the document and print each one
    expertise = doc.getElementsByTagName("expertise")
    print ("%d expertise:" % expertise.length)
    for skill in expertise:
        print (skill.getAttribute("name"))

    # create a new XML tag and add it into the document
    newexpertise = doc.createElement("expertise")
    newexpertise.setAttribute("name", "BigData")
    doc.firstChild.appendChild(newexpertise)
    print (" ")

    expertise = doc.getElementsByTagName("expertise")
    print ("%d expertise:" % expertise.length)
    for skill in expertise:
        print (skill.getAttribute("name"))

if __name__ == "__main__":
    main();
How to Parse XML using ElementTree

ElementTree is an API for manipulating XML. ElementTree is the easy way to process XML files.

We are using the following XML document as the sample data:

<data>
   <items>
      <item name="expertise1">SQL</item>
      <item name="expertise2">Python</item>
   </items>
</data>

Reading XML using ElementTree:

we must first import the xml.etree.ElementTree module.

import xml.etree.ElementTree as ET

Now let's fetch the root element:

root = tree.getroot()

Following is the complete code for reading above xml data

import xml.etree.ElementTree as ET
tree = ET.parse('items.xml')
root = tree.getroot()

# all items data
print('Expertise Data:')

for elem in root:
   for subelem in elem:
      print(subelem.text)

output:

Expertise Data:
SQL
Python
Summary:

Python enables you to parse the entire XML document at one go and not just one line at a time. In order to parse XML document you need to have the entire document in memory.

  • To parse XML document
  • Import xml.dom.minidom
  • Use the function "parse" to parse the document ( doc=xml.dom.minidom.parse (file name);
  • Call the list of XML tags from the XML document using code (=doc.getElementsByTagName( "name of xml tags")
  • To create and add new attribute in XML document
  • Use function "createElement"

What's Python IDLE? How to use Python IDLE to interact with Python?

What's Python IDLE? How to use Python IDLE to interact with Python?

In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.

If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!

In this tutorial, you’ll learn:

  • What Python IDLE is
  • How to interact with Python directly using IDLE
  • How to edit, execute, and debug Python files with IDLE
  • How to customize Python IDLE to your liking

Table of Contents

What Is Python IDLE?

Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.

Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.

An Interactive Interpreter

The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.

The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.

A File Editor

Every programmer needs to be able to edit and save text files. Python programs are files with the .py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.

Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!

How to Use the Python IDLE Shell

The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:

This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:

Here, you used print() to output the string "Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.

Next, take a look at the menu bar. You’ll see a few options for using the shell:

You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:

In the image above, you first declare a variable, x = 5. When you call print(x), the shell shows the correct output, which is the number 5. However, when you restart the shell and try to call print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable x is not defined. The shell has forgotten about everything that came before it was restarted.

You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:

A KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.

How to Work With Python Files

Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.

Opening a File

To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:

From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.

If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.

The content of this window will be the same as the paths that are returned when you call sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.

Editing a File

Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:

The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:

  1. The name of the file that you’re editing
  2. The full path to the folder where you can find this file on your computer
  3. The version of Python that IDLE is using

In the image above, you’re editing the file myFile.py, which is located in the Documents folder. The Python version is 3.7.1, which you can see in parentheses.

There are also two numbers in the bottom right corner of the window:

  1. Ln: shows the line number that your cursor is on.
  2. Col: shows the column number that your cursor is on.

It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.

There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:

The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the .py extension so that syntax highlighting will be enabled.

Executing a File

When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.

To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run python3 -i [filename] in your terminal.

When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.

How to Improve Your Workflow

Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.

Automatic Indentation

IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.

You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.

Code Completion and Call Tips

When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.

Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:

The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.

Code Context

The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.

To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:

As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.

How to Debug in IDLE

A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!

Interpreter DEBUG Mode

If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see [DEBUG ON] appear just before the prompt (>>>), which means the interpreter is ready and waiting.

When you execute your Python file, the debugger window will appear:

In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.

You can also click the following buttons to move through your code:

  • Go: Press this to advance execution to the next breakpoint. You’ll learn about these in the next section.
  • Step: Press this to execute the current line and go to the next one.
  • Over: If the current line of code contains a function call, then press this to step over that function. In other words, execute that function and go to the next line, but don’t pause while executing the function (unless there is a breakpoint).
  • Out: If the current line of code is in a function, then press this to step out of this function. In other words, continue the execution of this function until you return from it.

Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.

You’ll also see four checkboxes in the debug window:

  1. Globals: your program’s global information
  2. Locals: your program’s local information during execution
  3. Stack: the functions that run during execution
  4. Source: your file in the IDLE editor

When you select one of these, you’ll see the relevant information in your debug window.

Breakpoints

A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.

To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.

Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.

Errors and Exceptions

When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.

Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.

How to Customize Python IDLE

There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.

To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.

There are 5 areas of Python IDLE that you can customize:

  1. Fonts/Tabs
  2. Highlights
  3. Keys
  4. General
  5. Extensions

Let’s take a look at each of them now.

Fonts/Tabs

The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:

You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.

This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.

Highlights

The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.

Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:

  1. IDLE Day
  2. IDLE Night
  3. IDLE New

You can select from these pre-installed themes or create your own custom theme right in this window:

Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.

You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!

Keys

The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:

The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!

Creating Your Own Shortcuts

The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.

Select one pair from the list and press Get New Keys for Selection. A new window will pop up:

Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.

General

The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:

Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.

Extensions

The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.

To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!

Conclusion

In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

You’ve learned how to:

  • Work with the Python IDLE shell
  • Use Python IDLE as a file editor
  • Improve your workflow with features to help you code faster
  • Debug your code and view errors and exceptions
  • Customize Python IDLE to your liking

Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!

The easy way to work with CSV, JSON, and XML in Python

The easy way to work with CSV, JSON, and XML in Python

<strong>Originally published by </strong><a href="https://towardsdatascience.com/@george.seif94" target="_blank">George Seif</a><strong> </strong><em>at&nbsp;</em><a href="https://towardsdatascience.com/the-easy-way-to-work-with-csv-json-and-xml-in-python-5056f9325ca9" target="_blank">towardsdatascience.com</a>

Originally published by George Seif at towardsdatascience.com

Python’s superior flexibility and ease of use are what make it one of the most popular programming language, especially for Data Scientists. A big part of that is how simple it is to work with large datasets.

Every technology company today is building up a data strategy. They’ve all realised that having the right data: insightful, clean, and as much of it as possible, gives them a key competitive advantage. Data, if used effectively, can offer deep, beneath the surface insights that can’t be discovered anywhere else.

Over the years, the list of possible formats that you can store your data in has grown significantly. But, there are 3 that dominate in their everyday usage: CSV, JSON, and XML. In this article, I’m going to share with you the easiest ways to work with these 3 popular data formats in Python!

CSV Data

A CSV file is the most common way to store your data. You’ll find that most of the data coming from Kaggle competitions is stored in this way. We can do both read and write of a CSV using the built-in Python csv library. Usually, we’ll read the data into a list of lists.


Check out the code below. When we run csv.reader() all of our CSV data becomes accessible. The csvreader.next() function reads a single line from the CSV; every time you call it, it moves to the next line. We can also loop through every row of the csv using a for-loop as with for row in csvreader . Make sure that you have the same number of columns in each row, otherwise, you’ll likely end up running into some errors when working with your list of lists

import csv 
filename = "my_data.csv"
  
fields = [] 
rows = [] 
  
# Reading csv file 
with open(filename, 'r') as csvfile: 
    # Creating a csv reader object 
    csvreader = csv.reader(csvfile) 
      
    # Extracting field names in the first row 
    fields = csvreader.next() 
  
    # Extracting each data row one by one 
    for row in csvreader: 
        rows.append(row)
  
# Printing out the first 5 rows 
for row in rows[:5]: 
    print(row)

Writing to CSV in Python is just as easy. Set up your field names in a single list, and your data in a list of lists. This time we’ll create a writer() object and use it to write our data to file very similarly to how we did the reading.

import csv

# Field names 
fields = ['Name', 'Goals', 'Assists', 'Shots'] 
  
# Rows of data in the csv file 
rows = [ ['Emily', '12', '18', '112'], 
         ['Katie', '8', '24', '96'], 
         ['John', '16', '9', '101'], 
         ['Mike', '3', '14', '82']]
         
filename = "soccer.csv"
  
# Writing to csv file 
with open(filename, 'w+') as csvfile: 
    # Creating a csv writer object 
    csvwriter = csv.writer(csvfile) 
      
    # Writing the fields 
    csvwriter.writerow(fields) 
      
    # Writing the data rows 
    csvwriter.writerows(rows)

Of course, installing the wonderful Pandas library will make working with your data far easier once you’ve read it into a variable. Reading from CSV is a single line as is writing it back to file!

import pandas as pd

filename = "my_data.csv"


# Read in the data
data = pd.read_csv(filename)


# Print the first 5 rows
print(data.head(5))


# Write the data to file
data.to_csv("new_data.csv", sep=",", index=False)

We can even use Pandas to convert from CSV to a list of dictionaries with a quick one-liner. Once you have the data formatted as a list of dictionaries, we’ll use the dicttoxml library to convert it to XML format. We’ll also save it to file as a JSON!

import pandas as pd
from dicttoxml import dicttoxml
import json

# Building our dataframe
data = {'Name': ['Emily', 'Katie', 'John', 'Mike'],
        'Goals': [12, 8, 16, 3],
        'Assists': [18, 24, 9, 14],
        'Shots': [112, 96, 101, 82]
        }


df = pd.DataFrame(data, columns=data.keys())


# Converting the dataframe to a dictionary
# Then save it to file
data_dict = df.to_dict(orient="records")
with open('output.json', "w+") as f:
    json.dump(data_dict, f, indent=4)


# Converting the dataframe to XML
# Then save it to file
xml_data = dicttoxml(data_dict).decode()
with open("output.xml", "w+") as f:
    f.write(xml_data)

JSON Data

JSON provides a clean and easily readable format because it maintains a dictionary-style structure. Just like CSV, Python has a built-in module for JSON that makes reading and writing super easy! When we read in the CSV, it will become a dictionary. We then write that dictionary to file.

import json
import pandas as pd

# Read the data from file
# We now have a Python dictionary
with open('data.json') as f:
    data_listofdict = json.load(f)
    
# We can do the same thing with pandas
data_df = pd.read_json('data.json', orient='records')


# We can write a dictionary to JSON like so
# Use 'indent' and 'sort_keys' to make the JSON
# file look nice
with open('new_data.json', 'w+') as json_file:
    json.dump(data_listofdict, json_file, indent=4, sort_keys=True)


# And again the same thing with pandas
export = data_df.to_json('new_data.json', orient='records')

And as we saw before, once we have our data you can easily convert to CSV via pandas or use the built-in Python CSV module. When converting to XML, the dicttoxml library is always our friend.

import json
import pandas as pd
import csv

# Read the data from file
# We now have a Python dictionary
with open('data.json') as f:
    data_listofdict = json.load(f)
    
# Writing a list of dicts to CSV
keys = data_listofdict[0].keys()
with open('saved_data.csv', 'wb') as output_file:
    dict_writer = csv.DictWriter(output_file, keys)
    dict_writer.writeheader()
    dict_writer.writerows(data_listofdict)

XML Data

XML is a bit of a different beast from CSV and JSON. Generally, CSV and JSON are widely used due to their simplicity. They’re both easy and fast to read, write, and interpret as a human. There’s no extra work involved and parsing a JSON or CSV is very lightweight.


XML on the other hand tends to be a bit heavier. You’re sending more data, which means you need more bandwidth, more storage space, and more run time. But XML does come with a few extra features over JSON and CSV: you can use namespaces to build and share standard structures, better representation for inheritance, and an industry standardised way of representing your data with XML schema, DTD, etc.

To read in the XML data, we’ll use Python’s built-in XML module with sub-module ElementTree. From there, we can convert the ElementTree object to a dictionary using the xmltodictlibrary. Once we have a dictionary, we can convert to CSV, JSON, or Pandas Dataframe like we saw above!

import xml.etree.ElementTree as ET
import xmltodict
import json

tree = ET.parse('output.xml')
xml_data = tree.getroot()


xmlstr = ET.tostring(xml_data, encoding='utf8', method='xml')




data_dict = dict(xmltodict.parse(xmlstr))


print(data_dict)


with open('new_data_2.json', 'w+') as json_file:
    json.dump(data_dict, json_file, indent=4, sort_keys=True)

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