Planet Python

Last update: June 26, 2022 09:41 PM UTC

June 26, 2022

Zero to Mastery

Python Monthly Newsletter 💻🐍

31st issue of the Python Monthly Newsletter! Read by 25,000+ Python developers every month. This monthly Python newsletter covers the latest Python news so that you stay up-to-date with the industry and keep your skills sharp.

June 26, 2022 07:41 AM UTC

ItsMyCode

[Solved] AttributeError: module ‘time’ has no attribute ‘clock’

The time.clock() method has been removed in Python 3.8 onwards. Hence if you are using the clock() method in Python 3.8 or above, you will get AttributeError: module ‘time’ has no attribute ‘clock’.  

In this tutorial, we will look into what exactly is AttributeError: module ‘time’ has no attribute ‘clock’ and how to resolve the error with examples.

What is AttributeError: module ‘time’ has no attribute ‘clock’?

The time.clock() method has been deprecated from Python 3.8 onwards, which leads to an AttributeError: module ‘time’ has no attribute ‘clock’ if used in the code.

If you are not using the clock() method but still facing this error, that means you are using some of the Python libraries such as PyCrypto, sqlalchemy, etc., that internally use time.clock() method.

Let us try to reproduce the error in Python 3.8

import time

print(time.clock())

Output

AttributeError: module 'time' has no attribute 'clock'

How to fix AttributeError: module ‘time’ has no attribute ‘clock’?

There are multiple solutions to resolve this AttributeError and the solution depends on the different use cases. Let us take a look at each scenario.

Solution 1 – Replace time.clock() with time.process_time() and time.perf_counter()

Instead of the time.clock() method, we can use an alternate methods such as time.perf_counter() and time.process_time() that provides the same results.

Let us take an example to see how this can be implemented.

The time.perf_counter() function returns a float value of time in seconds i.e. a clock with the highest available resolution to measure a short duration. It does include time elapsed during sleep and is system-wide. 

import time

# perf_counter includes the execution time and the sleep time
start_time = time.perf_counter()
time.sleep(4)
end_time = time.perf_counter()

print("Elapsed Time is ", end_time-start_time)

Output

Elapsed Time is  4.0122200999903725

The time.process_time() method will return a float value of time in seconds. The time returned would be the sum of the system and user CPU time of the current process. It does not include time elapsed during sleep. 

import time

# perf_counter includes the execution time and the sleep time
start_time = time.process_time()
for i in range(1000):
    print(i, end=' ')
end_time = time.process_time()

print("Elapsed Time is ", end_time-start_time)

Output

Elapsed Time is  0.015625

Solution 2- Upgrade the module to the latest version

If you are directly using the time.clock() method in your code, then some of the external modules that you are using would be using it internally, and hence you are getting this error.

If you use a module like SQLAlchemy, upgrade it to the latest version using the below command.

pip install <module_name> --upgrade

pip install SQLAlchemy --upgrade

Solution 3 – Replace the module with another alternate

If you are using the modules like PyCrypto, there is no way you can upgrade it as it is not maintained actively.

In this case, you need to look for an alternate module like  PyCryptodome that supports the same functionality. 

# Uninstall the PyCrypto as its not maintained and install pycryptodome 
pip3 uninstall PyCrypto 
pip3 install pycryptodome 

Solution 4 – Downgrade the Python version

It is not a recommended approach to downgrade Python. However, if you have recently upgraded the Python version to 3.8 or above and facing this issue, it is better to revert or downgrade it back to the previous version until you find a solution or implement any of the above solutions.

Conclusion

The AttributeError: module ‘time’ has no attribute ‘clock’ occurs if you are using the time.clock() method in the Python 3.8 or above version. Another reason could be that you are using an external module that internally uses time.clock() function.

We can resolve the issue by using alternate methods such as time.process_time() and time.perf_counter() methods. If the issue is caused due to external modules, it is better to upgrade the module or find an alternative module if the module provides no fix.

June 26, 2022 12:46 AM UTC

June 25, 2022

Python for Beginners

List of Dictionaries to Dataframe in Python

Dataframes are mainly used in python for the analysis of tabular data. In this article, we will discuss how we can convert a list of dictionaries to a dataframe in python.

List of Dictionaries to Dataframe Using pandas.DataFrame()

The dataframe objects are defined in the pandas module. To create a dataframe from a given list of dictionaries, we can use the DataFrame() method. The DataFrame() method object takes a list of dictionaries as input argument and returns a dataframe created from the dictionaries. Here, the column names for the dataframe consist of the keys in the python dictionary. The values of each dictionary are transformed into the rows of the dataframe. You can create a dataframe from a list of dictionaries using the pandas.DataFrame() method as follows.

import pandas as pd

listOfDict = [{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'},
              {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
print("THe list of dictionaries is:")
print(listOfDict)
df = pd.DataFrame(listOfDict)
print("The dataframe is:")
print(df)

Output:

THe list of dictionaries is:
[{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'}, {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
The dataframe is:
     Name  Roll    Language
0  Aditya     1      Python
1     Sam     2        Java
2   Chris     3         C++
3    Joel     4  TypeScript

In the dataframe, a column is created for each key in the list of dictionaries. If a dictionary doesn’t have any specific key, the value NaN is given to the column corresponding to the specific key in the row representing the dictionary.

List of Dictionaries to Dataframe in Python Using pandas.dataframe.from_dict() Method

Instead of using the DataFrame() method, we can also use the from_dict() method to convert a list of dictionaries to a dataframe in Python. The from_dict() method takes a list of dictionaries as its input argument and returns a dataframe. Again, the column names for the dataframe consist of the keys in the dictionary. The values of each dictionary are transformed into the rows of the dataframe. You can create a dataframe from a list of dictionaries using the from_dict() method as follows.

import pandas as pd

listOfDict = [{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'},
              {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
print("THe list of dictionaries is:")
print(listOfDict)
df = pd.DataFrame.from_dict(listOfDict)
print("The dataframe is:")
print(df)

Output:

THe list of dictionaries is:
[{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'}, {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
The dataframe is:
     Name  Roll    Language
0  Aditya     1      Python
1     Sam     2        Java
2   Chris     3         C++
3    Joel     4  TypeScript

You can observe that the dataframe generated by the from_dict() method is similar to the dataframe generated by the DataFrame() method.

List of Dictionaries to Dataframe Using pandas.dataframe.from_records(Data)

To create a dataframe from a list of dictionaries in Python, we can also use the from_records() method. The from_records() method takes a list of dictionaries as its input argument and returns a dataframe. Again, the column names for the dataframe consist of the keys in the dictionary. The values of each dictionary are transformed into the rows of the dataframe. You can create a dataframe from a list of dictionaries using the from_records() method as follows.

import pandas as pd

listOfDict = [{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'},
              {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
print("THe list of dictionaries is:")
print(listOfDict)
df = pd.DataFrame.from_records(listOfDict)
print("The dataframe is:")
print(df)

Output:

THe list of dictionaries is:
[{'Name': 'Aditya', 'Roll': 1, 'Language': 'Python'}, {'Name': 'Sam', 'Roll': 2, 'Language': 'Java'}, {'Name': 'Chris', 'Roll': 3, 'Language': 'C++'}, {'Name': 'Joel', 'Roll': 4, 'Language': 'TypeScript'}]
The dataframe is:
     Name  Roll    Language
0  Aditya     1      Python
1     Sam     2        Java
2   Chris     3         C++
3    Joel     4  TypeScript

Conclusion

In this article, we have discussed three ways to convert a list of dictionaries to a dataframe in python. All three methods are semantically similar and produce the same result. To learn more about dictionaries, you can read this article on dictionary comprehension in python. You might also like this article on list comprehension in python.

The post List of Dictionaries to Dataframe in Python appeared first on PythonForBeginners.com.

June 25, 2022 01:00 AM UTC

June 24, 2022

Django Weblog

PyCharm & DSF Campaign 2022 Results

The sixth annual JetBrains PyCharm promotion in June netted the Django Software Foundation $25,000 this year.

This amount represents over 10% of the DSF's overall budget, which goes directly into funding the continued development and support of Django via the Django Fellowship program and Django conferences worldwide.

June 24, 2022 09:50 PM UTC

Mike Driscoll

Using Python to Turn Text-to-Speech on Mac OSX (Video)

Learn how to turn text into speech on Mac OSX with the Python programming language

The post Using Python to Turn Text-to-Speech on Mac OSX (Video) appeared first on Mouse Vs Python.

June 24, 2022 08:57 PM UTC

Robin Wilson

Update: recent work, conference talks and 3D printers

It’s been just over a year since I last posted here – and it’s been a busy year.

I’m aiming to start posting a bit more frequently (though I’ve said that before…), so I thought I’d start with an update of what I’ve been up to since my last post.

Work

Last October I started a new contract with Anglo American, a global mining and minerals company. They brought me in to work develop a new system called Image Hub, which will provide a central place to store, search, visualise and process image data. We’re focusing mainly on satellite and aerial images at the moment, but will be expanding to other sorts of geospatial and non-geospatial in the future.

I started as part of a very small team (me and one other person), evaluating a range of technologies to solve this problem. We then progressed to building some prototypes, and now have an initial working system in pre-production. We’re starting to get more people on the team and are working towards getting a full system in production.

I’ve really enjoyed getting stuck into the Cloud Native Geospatial ecosystem – including Spatio-Temporal Asset Catalogs, Cloud-Optimized GeoTIFFs and the various associated technologies. I’d used these before as a data consumer, but now I’ve been on the other side, setting up STAC API servers, converting data to COGs. setting up Tilers.

I attended the Cloud Native Geospatial Day 2022 and gave a talk on our work at Anglo, which you can watch below:

Although my work with Anglo is taking most of my time, I’ve kept going with a bit of other work including some consulting on Py6S.

Electromagnetic Field

I attended Electromagnetic Field with my family during the May half-term. EMF is a science/technology/electronics/computing/general-geek festival held in a field in Herefordshire. We camped there (the first time my son had been camping) and had a wonderful time.

I did a couple of workshops where I built a Z80-based computer and a Geiger counter and my wife did some soldering workshops and a blacksmithing workshop. My son (only 5 years old) even made and launched a solid-fuelled rocket!

As well as all the workshops there were some fascinating talks – and I also gave a talk titled Learning from accidents: an introduction to railway signalling in the UK. It was heavily based on one of the talks that I give to science/technology societies (see my talks page) but extended and altered a bit for the EMF audience. All the talks were videoed, so I’ll post a link here when the video is available.

Fun evening activities

Since coming back from EMF I’ve been inspired to do some interesting things in the evenings. This is partly because a friend gave me a 3D printer at EMF, so I’ve been having fun getting to grips with that. I’ve also had fun using the things I made in the workshops at EMF. Some of the things I’ve been up to include:

• Designing (using Fusion 360) and 3D printing a mini balloon-powered hovercraft
• Printing a load of cool 3D prints from sites like Thingiverse
• Connecting my Geiger counter (from EMF) to a Raspberry Pi (and later to an Arduino, and the EMF badge) to get count data out, and to generate true random numbers
• Writing programs in Z80 assembler (just like my father did many years ago) – which has been fascinating, and a nice change from the modern ‘wire all these APIs together and try and work out which dependency has a bug that’s causing your problem’ approach to programming

I’m hoping to write some posts here on some of these activities – stay tuned to hear more…

June 24, 2022 08:56 PM UTC

Real Python

The Real Python Podcast – Episode #115: Digging Into PyScript & Preventing or Handling Python Errors

Have you heard about PyScript? The brand-new framework has the community excited about building interactive Python applications that run entirely within the user's browser. Would you like to dig into the details beyond the "Hello World" examples? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

June 24, 2022 12:00 PM UTC

June 23, 2022

Mike Driscoll

An Intro to Kivy Layouts (Video)

Learn how to layout your widgets in Kivy, a mobile GUI framework for Python. Kivy is a cross-platform GUI framework that also works on iOS and Android.

The examples in this video are based on the code from Kivy 101: How to Use BoxLayouts

The post An Intro to Kivy Layouts (Video) appeared first on Mouse Vs Python.

June 23, 2022 06:24 PM UTC

PyBites

Common Python developer pitfalls and the 80/20 that really matters

Listen here:

Welcome back to another Pybites podcast episode. In this episode we talk about common pitfalls you want to avoid when becoming a Python developer:

Pitfall #1: Tutorial paralysis
Pitfall #2: Improper sequencing
Pitfall #3: Obsessing over Pythonic code
Pitfall #4: Going on your own for too long

… after that we look at the 80/20 (aka “Pareto”) to focus on  to become a well-rounded Python developer:

80/20 Rule #1: Work on bigger projects
80/20 Rule #2: Build a portfolio
80/20 Rule #3: Work with experienced developers
80/20 Rule #4: Become a content provider

—
If this resonates with you and you want to better your career as a Pythonista, book us in for a career chat: https://go.oncehub.com/pybites

Thanks for listening,  Any feedback, write us an email to info@pybit.es

We’ll be back next week!

June 23, 2022 02:50 PM UTC

PyCharm

On Tuesday, June 21, 2022, Matt Harrison joined Jodie Burchell for a webinar about Pandas, and how to be more effective while working with this popular library. Here’s the recording for you to watch if you missed the live stream.

Description

In this webinar Matt Harrison and Jodie Burchell discussed tips for making Pandas more memory-friendly, getting the best performance possible when applying operations to Series and DataFrames, keeping your Pandas code as reproducible and tidy as possible, and how using the right tooling can make working with Pandas easier. They discussed how to deal with trickier topics in data processing such as feature engineering and missing data. Finally, they talked about some user-submitted tips which help get the best out of Pandas.

Speaking to You

Matt Harrison runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage. He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON, as well as local user conferences. He has written a number of books on Python and Data Science, including Machine Learning Pocket Reference, Pandas 1.x Cookbook, Effective PyCharm, and Effective Pandas. He blogs at hairysun.com.

Dr. Jodie Burchell is a Developer Advocate for Data Science at JetBrains and was previously the Lead Data Scientist in audiences generation at Verve Group Europe. After finishing a PhD in psychology and a postdoc in biostatistics, she has worked in a range of data science and machine learning roles across search improvement, recommendation systems, natural language processing, and programmatic advertising. She is also the author of two books, The Hitchhiker’s Guide to Ggplot2 and The Hitchhiker’s Guide to Plotnine, and blogs at t-redactyl.io.

June 23, 2022 10:39 AM UTC

Webinar Recording: “Idiomatic Pandas: 5 Tips for Better Pandas”, With Matt Harrison

June 23, 2022 10:02 AM UTC

Matt Layman

Refactoring and New Features - Building SaaS with Python and Django #137

In this episode, I continued on the teacher checklist for the homeschooling app. We added data to the context and this required some refactoring to be able to reuse data from other parts of the app.

June 23, 2022 12:00 AM UTC

June 22, 2022

ItsMyCode

[Solved] AttributeError: ‘tuple’ object has no attribute ‘get’

The AttributeError: ‘tuple’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the tuple value. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘tuple’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘tuple’ object has no attribute ‘get’?

If we call the get() method on the tuple value, Python will raise an AttributeError: ‘tuple’ object has no attribute ‘get’. The error can also happen if you have a method which returns an tuple instead of a dictionary.

Let us take a simple example to reproduce this error.

# Method return None instead of dict
def fetch_data():
    output = ("Audi", "BMW")
    return output


data = fetch_data()
print(data.get("name"))

Output

AttributeError: 'tuple' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an tuple instead of a dictionary.

Since we call the get() method on the tuple value, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return None instead of dict
def fetch_data():
    output = ("Audi", "BMW")
    return output


data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'tuple'>

List of valid attributes in this object is  ['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']

How to fix AttributeError: ‘tuple’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the tuple type.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method returns dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Get the  car Name
print(data.get("name"))

Output

Audi

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method returns dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (type(data) == dict):
    print(data.get("name"))


softwares = ("Audi", "BMW")

if (type(softwares) == dict):
    print(softwares.get("name"))
else:
    print("The object is not dictionary and it is of type ", type(softwares))

Output

Audi
The object is not dictionary and it is of type  <class 'tuple'>

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method returns dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (hasattr(data, 'get')):
    print(data.get("name"))


# Incase of None value
softwares =  ("Audi", "BMW")

if (hasattr(softwares, 'get')):
    print(softwares.get("name"))
else:
    print("The object does not have get attribute")

Output

Audi
The object does not have get attribute

Conclusion

The AttributeError: ‘tuple’ object has no attribute ‘get’ occurs when you try to call the get() method on the None type. The error also occurs if the calling method returns an tuple instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object instead of an tuple. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 09:05 PM UTC

[Solved] AttributeError: ‘NoneType’ object has no attribute ‘get’

The AttributeError: ‘NoneType’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the None value. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘NoneType’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘NoneType’ object has no attribute ‘get’?

If we call the get() method on the None value, Python will raise an AttributeError: ‘NoneType’ object has no attribute ‘get’. The error can also happen if you have a method which returns an None instead of a dictionary or if we forget the return statement in the function as shown below.

Let us take a simple example to reproduce this error.

# Method return None instead of dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}


data = fetch_data()
print(data.get("name"))

Output

AttributeError: 'NoneType' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an None instead of a dictionary because the return statement is missing.

Since we call the get() method on the None value, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return None instead of dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}


data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'NoneType'>

List of valid attributes in this object is  ['__bool__', '__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']
PS C:\Personal\IJS\Code>

How to fix AttributeError: ‘NoneType’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the None type.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method return None instead of dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Get the  car Name
print(data.get("name"))

Output

Audi

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method return None instead of dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (type(data) == dict):
    print(data.get("name"))


softwares = None

if (type(softwares) == dict):
    print(softwares.get("name"))
else:
    print("The object is not dictionary and it is of type ", type(softwares))

Output

Audi
The object is not dictionary and it is of type  <class 'NoneType'>

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method return None instead of dict
def fetch_data():
    output = {"name": "Audi",  "price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (hasattr(data, 'get')):
    print(data.get("name"))


# Incase of None value
softwares = None

if (hasattr(softwares, 'get')):
    print(softwares.get("name"))
else:
    print("The object does not have get attribute")

Output

Audi
The object does not have get attribute

Conclusion

The AttributeError: ‘NoneType’ object has no attribute ‘get’ occurs when you try to call the get() method on the None type. The error also occurs if the calling method returns an None instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object instead of an None. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 08:56 PM UTC

[Solved] AttributeError: ‘str’ object has no attribute ‘get’

The AttributeError: ‘str’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the string data type. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘str’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘str’ object has no attribute ‘get’?

If we call the get() method on the string data type, Python will raise an AttributeError: ‘str’ object has no attribute ‘get’. The error can also happen if you have a method which returns an string instead of a dictionary.

Let us take a simple example to reproduce this error.

# Method return string instead of dict
def fetch_data():
    output = "Toyota Car"
    return output


data = fetch_data()
print(data.get("name"))

Output

AttributeError: 'str' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an string instead of a dictionary.

Since we call the get() method on the string type, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return string instead of dict
def fetch_data():
    output = "Toyota Car"
    return output


data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'str'>

List of valid attributes in this object is  ['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isascii', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'removeprefix', 'removesuffix', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']

How to fix AttributeError: ‘str’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the string type.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output

data = fetch_data()

# Get the  car Name
print(data.get("Name"))

Output

Audi

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (type(data) == dict):
    print(data.get("Name"))


softwares = "Norton, Bit Defender"

if (type(softwares) == dict):
    print(softwares.get("Name"))
else:
    print("The object is not dictionary and it is of type ", type(softwares))

Output

Audi
The object is not dictionary and it is of type  <class 'str'>

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output


data = fetch_data()

# Check if the object has get attribute
if (hasattr(data, 'get')):
    print(data.get("Name"))

Output

Audi

Conclusion

The AttributeError: ‘str’ object has no attribute ‘get’ occurs when you try to call the get() method on the string data type. The error also occurs if the calling method returns an string instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object instead of an string. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 08:48 PM UTC

[Solved] AttributeError: ‘float’ object has no attribute ‘get’

The AttributeError: ‘float’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the float data type. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘float’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘float’ object has no attribute ‘get’?

If we call the get() method on the float data type, Python will raise an AttributeError: ‘float’ object has no attribute ‘get’. The error can also happen if you have a method which returns an float instead of a dictionary.

Let us take a simple example to reproduce this error.

# Method return float instead of dict
def fetch_data():
    output = 44.55
    return output


data = fetch_data()
print(data.get("price"))

Output

AttributeError: 'float' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an float instead of a dictionary.

Since we call the get() method on the float type, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return float instead of dict
def fetch_data():
    output = "Toyota Car"
    return output


data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'float'>

List of valid attributes in this object is  ['__abs__', '__add__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getformat__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__int__', '__le__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__pos__', '__pow__', '__radd__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rmod__', '__rmul__', '__round__', '__rpow__', '__rsub__', '__rtruediv__', '__set_format__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', 'as_integer_ratio', 'conjugate', 'fromhex', 'hex', 'imag', 'is_integer', 'real']

How to fix AttributeError: ‘float’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the float type.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "NordVPN",  "Price": "22.22"}
    return output


data = fetch_data()

# Get the Price
print(data.get("Price"))

Output

22.22

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "NordVPN",  "Price": "22.22"}
    return output


data = fetch_data()

# Get the Price
if (type(data) == dict):
    print(data.get("Price"))
else:
    print("The object is not dictionary and it is of type ", type(data))

Output

22.22

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method returns dict
def fetch_data():
    output = {"Name": "NordVPN",  "Price": "22.22"}
    return output


data = fetch_data()

# Get the Price
if (hasattr(data, 'get')):
    print(data.get("Price"))
else:
    print("The object does not have get attribute")

Output

22.22

Conclusion

The AttributeError: ‘float’ object has no attribute ‘get’ occurs when you try to call the get() method on the floatdata type. The error also occurs if the calling method returns an float instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object instead of an float. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 08:07 PM UTC

[Solved] AttributeError: ‘list’ object has no attribute ‘get’

The AttributeError: ‘list’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the list data type. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘list’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘list’ object has no attribute ‘get’?

If we call the get() method on the list data type, Python will raise an AttributeError: ‘list’ object has no attribute ‘get’. The error can also happen if you have a method which returns an list instead of a dictionary.

Let us take a simple example to reproduce this error.

# Method return list of dict 
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars


data = fetch_data()
print(data.get("name"))

Output

AttributeError: 'list' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an list of dictionary object instead of a dictionary.

Since we call the get() method directly on the list type, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return list of dict 
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars

data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'list'>

List of valid attributes in this object is  ['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

How to fix AttributeError: ‘list’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the list type.

Since the data has a valid dictionary object inside the list, we can loop through the list and use the get() method on the dictionary elements.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method return list of dict 
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars


data = fetch_data()
for item in data:
    print(item.get("name"))

Output

Audi
Ferrari
BMW

We can also use the generator expression, filter function to get specific dictionary element, or directly use the index of the list. Let us look at each of these with examples.

# Method return list instead of dict
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars


data = fetch_data()
# Generator expression to get specific element
car_obj = next(
    (x for x in data if x['name'] == 'BMW'),
    {}
)

print(car_obj)
print("Car Name is ", car_obj.get("name"))
print("Car Price is ", car_obj.get("price"))

# Directly access the dictionary using the index of list
print("The car name in index 0 is ", data[0].get("name"))

Output

{'name': 'BMW', 'price': 55000}
Car Name is  BMW
Car Price is  55000

The car name in index 0 is  Audi

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method return list of dict
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars

# assigns the list of dict
data = fetch_data()
if (type(data) == dict):
    print(data.get("name"))
else:
    print("The object is not dictionary and it is of type ", type(data))

# assign the index 0 dict    
my_car =data[0]
if (type(my_car) == dict):
    print(my_car.get("name"))
else:
    print("The object is not dictionary and it is of type ", type(my_car))

Output

The object is not dictionary and it is of type  <class 'list'>
Audi

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method return list of dict
def fetch_data():
    cars = [
        {'name': 'Audi', 'price': 45000},
        {'name': 'Ferrari', 'price': 450000},
        {'name': 'BMW', 'price': 55000},
    ]
    return cars


# assigns the list of dict
data = fetch_data()
if (hasattr(data, 'get')):
    print(data.get("name"))
else:
    print("The object does not have get attribute")

# assign the index 0 dict
my_car = data[0]
if (hasattr(my_car, 'get')):
    print(my_car.get("name"))
else:
    print("The object does not have get attribute")

Output

The object does not have get attribute
Audi

Conclusion

The AttributeError: ‘list’ object has no attribute ‘get’ occurs when you try to call the get() method directly on the list data type. The error also occurs if the calling method returns an list instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object by iterating the list instead of directly calling the get() method on an list. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 07:56 PM UTC

[Solved] AttributeError: ‘int’ object has no attribute ‘get’

The AttributeError: ‘int’ object has no attribute ‘get’ mainly occurs when you try to call the get() method on the integer type. The attribute get() method is present in the dictionary and must be called on the dictionary data type.

In this tutorial, we will look at what exactly is AttributeError: ‘int’ object has no attribute ‘get’ and how to resolve this error with examples.

What is AttributeError: ‘int’ object has no attribute ‘get’?

If we call the get() method on the integer data type, Python will raise an AttributeError: ‘int’ object has no attribute ‘get’. The error can also happen if you have a method which returns an integer instead of a dictionary.

Let us take a simple example to reproduce this error.

# Method return integer instead of dict
def fetch_data():
    output = 100
    return output


data = fetch_data()
print(data.get("name"))

Output

AttributeError: 'int' object has no attribute 'get'

In the above example, we have a method fetch_data() which returns an integer instead of a dictionary.

Since we call the get() method on the integer type, we get AttributeError.

We can also check if the variable type using the type() method, and using the dir() method, we can also print the list of all the attributes of a given object.

# Method return integer instead of dict
def fetch_data():
    output = 100
    return output


data = fetch_data()
print("The type of the object is ", type(data))
print("List of valid attributes in this object is ", dir(data))

Output

The type of the object is  <class 'int'>

List of valid attributes in this object is  ['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_count', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']

How to fix AttributeError: ‘int’ object has no attribute ‘get’?

Let us see how we can resolve the error.

Solution 1 – Call the get() method on valid dictionary

We can resolve the error by calling the get() method on the valid dictionary object instead of the integer type.

The dict.get() method returns the value of the given key. The get() method will not throw KeyError if the key is not present; instead, we get the None value or the default value that we pass in the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output

data = fetch_data()

# Get the price of the car
print(data.get("Price"))

Output

$45000

Solution 2 – Check if the object is of type dictionary using type

Another way is to check if the object is of type dictionary; we can do that using the type() method. This way, we can check if the object is of the correct data type before calling the get() method.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output


data = fetch_data()

# Check if the object is dict
if (type(data) == dict):
    print(data.get("Price"))

Output

$45000

Solution 3 – Check if the object has get attribute using hasattr

Before calling the get() method, we can also check if the object has a certain attribute. Even if we call an external API which returns different data, using the hasattr() method, we can check if the object has an attribute with the given name.

# Method returns dict
def fetch_data():
    output = {"Name": "Audi", "Release_Date": "2022", "Price": "$45000"}
    return output


data = fetch_data()

# Check if the object has get attribute
if (hasattr(data, 'get')):
    print(data.get("Price"))

Output

$45000

Conclusion

The AttributeError: ‘int’ object has no attribute ‘get’ occurs when you try to call the get() method on the integer type. The error also occurs if the calling method returns an integer instead of a dictionary object.

We can resolve the error by calling the get() method on the dictionary object instead of an integer. We can check if the object is of type dictionary using the type() method, and also, we can check if the object has a valid get attribute using hasattr() before performing the get operation.

June 22, 2022 07:16 PM UTC

PyCharm

PyCharm 2022.1.3 Is Out!

We just released PyCharm 2022.1.3, which provides fixes for the important regressions that appeared in 2022.1 and 2022.1.1.

Please note: If you already have PyCharm 2022.1.2 installed, you will need to update to PyCharm 2022.1.3 manually via the Toolbox App or the website.

Download PyCharm 2022.1.3!

Here’s what we’ve done:

• Fixed a regression with the debug console that was truncating outputs [PY-53983].
• Fixed a regression causing .pth files to be ignored inside venv site-packages [PY-54321].
• Cmd+Click / Ctrl+Click in the Git Log panel has been fixed and now works as expected [IDEA-292405].
• The New Project button on the Welcome screen is once again working as intended [IDEA-276553].
• Fixed the issue causing a misleading error message when using $var in the calc() function in SCSS files [WEB-54056].
• Fixed the issue causing an Unexpected term error when using a variable in a min() and max() arguments list [WEB-52057].

For the full list of bug fixes, please take a look at the release notes. If you notice any bugs, please submit them to our issue tracker.

The PyCharm team

June 22, 2022 07:13 PM UTC

Python Morsels

Appreciating Python's match-case by parsing Python code

Python's match-case blocks are complex structural pattern matching tools that are often more hassle than they're worth. But they're great for parsing abstract syntax trees.

Read the full article: https://www.pythonmorsels.com/match-case-parsing-python/

June 22, 2022 06:16 PM UTC

Real Python

Effective Python Testing With Pytest

PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install pytest

$ python -m venv venv
$ source venv/bin/activate
(venv) $ python -m pip install pytest

# test_with_unittest.py

from unittest import TestCase

class TryTesting(TestCase):
    def test_always_passes(self):
        self.assertTrue(True)

    def test_always_fails(self):
        self.assertTrue(False)

(venv) $ python -m unittest discover
F.
======================================================================
FAIL: test_always_fails (test_with_unittest.TryTesting)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "...\effective-python-testing-with-pytest\test_with_unittest.py",
  line 10, in test_always_fails
    self.assertTrue(False)
AssertionError: False is not true

----------------------------------------------------------------------

Ran 2 tests in 0.006s

FAILED (failures=1)

Read the full article at https://realpython.com/pytest-python-testing/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

June 22, 2022 02:00 PM UTC

Python for Beginners

Append Dictionary to CSV File in Python

CSV files are one of the most efficient tools to store structured, tabular data. Sometimes, we might need to append data to the CSV file from a python dictionary. In this article, we will discuss how we can append the values in a dictionary to a CSV file in python. 

Append Dictionary to CSV File Using csv.writer()

You can append a dictionary to CSV file using CSV.writer() method and CSV.writerow() method. For this, we will first open the CSV file in append mode using the open() function. The open() function takes the filename as its first input argument and the literal “a” as its second input argument to denote that the file is opened in the append mode.

After opening the file, we will create a CSV writer object using the CSV.writer() function. The CSV.writer() function takes the file object containing the CSV file as its input argument and returns a writer object. 

After creating the writer object, we will append the dictionary to the CSV file using the writerow() method. The writerow() method, when invoked on a writer object, takes the values in the dictionary as its input argument and appends it to the CSV file.

After execution of the writerow() method, you must close the CSV file using the close() method. Otherwise, the changes won’t be saved in the CSV file. The source code for this approach to append a dictionary to a CSV file is given below.

import csv

myFile = open('Demo.csv', 'r+')
print("The content of the csv file before appending is:")
print(myFile.read())
myDict = {'Roll': 4, 'Name': 'Joel', 'Language': 'Golang'}
print("The dictionary is:")
print(myDict)
writer = csv.writer(myFile)
writer.writerow(myDict.values())
myFile.close()
myFile = open('Demo.csv', 'r')
print("The content of the csv file after appending is:")
print(myFile.read())

Output:

The content of the csv file before appending is:
Roll,Name,Language
1,Aditya,Python
2,Sam, Java
3, Chris, C++

The dictionary is:
{'Roll': 4, 'Name': 'Joel', 'Language': 'Golang'}
The content of the csv file after appending is:
Roll,Name,Language
1,Aditya,Python
2,Sam, Java
3, Chris, C++
4,Joel,Golang

Append Dictionary to CSV File using csv.DictWriter()

Instead of using the csv.writer() method, we can use the csv.DictWriter() function along with the csv.writerow() method to append a python dictionary to csv file. The approach is almost similar to the approach using the csv.writer() method with the following differences.

• Instead of the csv.writer() method, we will use the csv.DictWriter() method. The DictWriter() method takes the file object containing the csv file as its input argument and returns a DictWriter object.
• When the writerow() method is executed on the DictWriter object, it takes a dictionary as the input argument instead of the values in the dictionary.

The python code to append a dictionary to a csv file using csv.DictWriter() is as follows.

import csv

myFile = open('Demo.csv', 'r+')
print("The content of the csv file before appending is:")
print(myFile.read())
myDict = {'Roll': 4, 'Name': 'Joel', 'Language': 'Golang'}
print("The dictionary is:")
print(myDict)
writer = csv.DictWriter(myFile, fieldnames=list(myDict.keys()))
writer.writerow(myDict)
myFile.close()
myFile = open('Demo.csv', 'r')
print("The content of the csv file after appending is:")
print(myFile.read())

Output:

The content of the csv file before appending is:
Roll,Name,Language
1,Aditya,Python
2,Sam, Java
3, Chris, C++


The dictionary is:
{'Roll': 4, 'Name': 'Joel', 'Language': 'Golang'}
The content of the csv file after appending is:
Roll,Name,Language
1,Aditya,Python
2,Sam, Java
3, Chris, C++

4,Joel,Golang

Conclusion

In this article, we have discussed two ways to append a dictionary to csv file in python. In these approaches, the dictionary will be appended irrespective of whether it has same number of items as compared to the columns in the csv file or it has the same keys as compared to the column names in the csv file. Thus it advised to make sure that the dictionary should have the same number of keys as compared to the columns in the csv file. Also You should make sure that the order of columns of the csv file should be same as the order of the keys present in the dictionary. Otherwise, the data appended to the csv file will become inconsistent and will lead to errors. 

I hope you enjoyed reading this article.To know more about dictionaries in python, you can read this article on dictionary comprehension in python. You might also write this article on list comprehension in python.

Stay tuned for more informative articles.

Happy Learning!

The post Append Dictionary to CSV File in Python appeared first on PythonForBeginners.com.

June 22, 2022 01:00 PM UTC

Talk Python to Me

#370: OpenBB: Python's Open-source Investment Platform

You may have heard of the Bloomberg terminal. It's expensive software that can monitor and analyze real-time financial market data and place trades on the electronic trading platform. But have you heard of OpenBB? It's similar software for real-time and long term analysis for finance and investing. The difference is it's open source and built entirely with Python and gives you access to analyze a massive amount of real-time and historical data using the full Python data science stack. On this episode, we have one of the cofounders, James Maslek here to give us a look inside this cool piece of Python-based software.<br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>James Maslek</b>: <a href="https://www.linkedin.com/in/james-maslek/" target="_blank" rel="noopener">linkedin.com</a><br/> <b>OpenBB</b>: <a href="https://www.openbb.co" target="_blank" rel="noopener">openbb.co</a><br/> <b>OpenBB Feature Gallery</b>: <a href="https://openbb.co/products/terminal" target="_blank" rel="noopener">openbb.co</a><br/> <b>$8.5M seed funding announcement</b>: <a href="https://www.openbb.co/blog/gme-didnt-take-me-to-the-moon-but-gamestonk-terminal-did" target="_blank" rel="noopener">openbb.co/blog</a><br/> <b>How to get rich talk by Naval (less money-focused than the title implies)</b>: <a href="https://www.youtube.com/watch?v=1-TZqOsVCNM" target="_blank" rel="noopener">youtube.com</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=BbqvRqMbVFU" target="_blank" rel="noopener">youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/370/openbb-pythons-open-source-investment-platform" target="_blank" rel="noopener">talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="https://talkpython.fm/youtube" target="_blank" rel="noopener">youtube.com</a><br/> <b>Follow Talk Python on Twitter</b>: <a href="https://twitter.com/talkpython" target="_blank" rel="noopener">@talkpython</a><br/> <b>Follow Michael on Twitter</b>: <a href="https://twitter.com/mkennedy" target="_blank" rel="noopener">@mkennedy</a><br/></div><br/> <strong>Sponsors</strong><br/> <a href='https://talkpython.fm/sentry'>Sentry Error Monitoring, Code TALKPYTHON</a><br> <a href='https://talkpython.fm/python-at-scale'>Python at Scale</a><br> <a href='https://talkpython.fm/assemblyai'>AssemblyAI</a><br> <a href='https://talkpython.fm/training'>Talk Python Training</a>

June 22, 2022 08:00 AM UTC

Python Anywhere

Anaconda Acquisition FAQs

In June 2022, Anaconda announced its acquisition of PythonAnywhere. This acquisition expands Python team collaboration in the cloud and adds capabilities designed to unite teams and create access to more robust cloud resources. We’ve compiled the following list of frequently asked questions to aid users in navigating the transition. – Will this affect my PythonAnywhere account? For now, it won’t! There are no plans to change PythonAnywhere user accounts, and you can continue to use yours as usual without interruption.

June 22, 2022 12:00 AM UTC

June 21, 2022

PyCoder’s Weekly

Issue #530 (June 21, 2022)

#530 – JUNE 21, 2022
View in Browser »

ButterCMS Melts Into Your Python app. Try the #1 Headless CMS for Python and Django

ButterCMS is your content backend. Enable your marketing team to update website + app content without bothering you. Try the #1 rated Headless CMS for Python today. Free for 30 days →
BUTTERCMS sponsor

Discussions

Python Jobs

Senior Python Software Engineer (San Francisco, CA, USA)

MyOme

Senior Full-Stack Web Developer (Anywhere)

MonetizeMore

Backend Software Developer (Draper, UT, USA)

Canopy

Gameful Learning Developer (Ann Arbor, MI, USA)

University of Michigan

Python Technical Architect (USA)

Blenderbox

Software Engineer (Los Angeles or Dallas) (Los Angeles, CA, USA)

Causeway Capital Management LLC

DevOps Engineer (Ann Arbor, MI, USA)

University of Michigan

Academic Innovation Developer (Ann Arbor, MI, USA)

University of Michigan

Software Development Lead (Ann Arbor, MI, USA)

University of Michigan

Principal Python Engineer (100% Remote) (San Francisco, CA, USA)

Nira

More Python Jobs >>>

Articles & Tutorials

Connect, Integrate, & Automate Your Data—From Python, or Any Other Application or Tool

CData makes it easier to unlock the value of data — simplifying connectivity between applications and data sources. Our SQL-based connectors streamline data access making it easy to access real-time data from on-premise and cloud databases, SaaS, APIs, NoSQL and more. Visit cdata.com to learn more →
CDATA SOFTWARE sponsor

Projects & Code

Events

Happy Pythoning!
This was PyCoder’s Weekly Issue #530.
View in Browser »

[ Subscribe to 🐍 PyCoder’s Weekly 💌 – Get the best Python news, articles, and tutorials delivered to your inbox once a week >> Click here to learn more ]

June 21, 2022 07:30 PM UTC

• • RSS feed
  • Titles Only
  • Powered by Planet!
• Other Python Planets
  • Python Summer of Code
  • Planet Python Francophone
  • Planet Python Argentina
  • Planet Python Brasil
  • Planet Python Poland
• Python Libraries
  • OLPC
  • PySoy
  • SciPy
  • SymPy
  • Twisted
• Python/Web Planets
  • CherryPy
  • Django Community
  • Plone
  • Turbogears
• Other Languages
  • Haskell
  • Lisp
  • Parrot
  • Perl
  • Ruby
• Databases
  • MySQL
  • PostgreSQL
• Subscriptions
  • [OPML feed]
  • "Mathspp Pydon'ts"
  • "Menno's Musings"
  • "Michael J.T. O'Kelly"
  • "Morphex's Blogologue"
  • "Speno's Pythonic Avocado"
  • "William's Journal"
  • 2degrees
  • A. Jesse Jiryu Davis
  • ABlog for Sphinx
  • Aahz
  • Abhijeet Pal
  • Abu Ashraf Masnun
  • Adam Pletcher
  • Agendaless Consulting
  • Ahmed Bouchefra
  • Al-Ahmadgaid Asaad
  • Alec Munro
  • Alex Grönholm
  • Alex Morozov
  • Alexander Limi
  • Alexandre Conrad
  • Alexandre Vassalotti
  • Alexey Evseev
  • Allison Kaptur
  • Amjith Ramanujam
  • AmvTek
  • Anarcat
  • Anatoly Techtonik
  • Andre Roberge
  • Andrea Grandi
  • Andrew Dalke
  • Andriy Kornatskyy
  • Andy Dustman
  • Andy R. Terrel
  • Anna Martelli Ravenscroft
  • Anthony Baxter
  • Anton Belyaev
  • Anton Bobrov
  • Anwesha Das
  • Armin Ronacher
  • Artem Golubin
  • Artem Rys
  • Ashish Vidyarthi
  • Astro Code School
  • Automating OSINT
  • Awesome Python Applications
  • Baiju Muthukadan
  • Bajusz Tamás
  • BeDjango
  • Ben Bass
  • Ben Cook
  • Ben Rousch
  • Benjamin Peterson
  • Benji York
  • Bertrand Mathieu
  • Bhavin Gandhi
  • Bhishan Bhandari
  • BioPython News
  • Bit of Cheese
  • Bojan Mihelac
  • Brandon Rhodes
  • BreadcrumbsCollector
  • Brendan Scott
  • Brett Cannon
  • Brian Okken
  • Bruno Oliveira
  • Bruno Rocha
  • Caktus Consulting Group
  • Calvin Cheng
  • Calvin Spealman
  • Carl Chenet
  • Carl Düvel
  • Carl Trachte
  • Carlos Eduardo de Paula
  • Casey Duncan
  • Catherine Devlin
  • Ching-Hwa Yu
  • Chris Hager
  • Chris Miles
  • Chris Mitchell
  • Chris Moffitt
  • Chris Rose
  • Chris Warrick
  • Christian Heimes
  • Christian Scholz
  • Christoph Zwerschke
  • CodeGrades
  • CodeSnipers
  • Codementor
  • CodersLegacy
  • Coding Diet
  • Corey Gallon
  • Corey Goldberg
  • Cormoran Project
  • Cross-Platform Command Line Tools
  • CubicWeb
  • Curtis Miller
  • DSPIllustrations.com
  • DaPythonista
  • Daily Tech Video (Python)
  • Dallas Fort Worth Pythoneers
  • Dan Crosta
  • Dan Stromberg
  • Dan Yeaw
  • Daniel Bader
  • Daniel Nouri
  • Daniel Roy Greenfeld
  • Data Community DC
  • Data School
  • Dave Beazley
  • David Amos
  • David Caron
  • David Goodger
  • David Lindelof
  • David MacIver
  • David Malcolm
  • David Szotten
  • Davide Moro
  • Davy Mitchell
  • Davy Wybiral
  • Denis Kurov
  • Django Weblog
  • Django Weekly
  • Djangostars
  • Doing Math with Python
  • Doug Hellmann
  • Dougal Matthews
  • Duncan McGreggor
  • Ed Crewe
  • Edward K. Ream
  • Eli Bendersky
  • Eniram Ltd.
  • Eray Özkural (examachine)
  • Erik Marsja
  • Etienne Desautels
  • EuroPython
  • EuroPython Society
  • Evennia
  • Everyday Superpowers
  • Fabio Zadrozny
  • Filip Wasilewski
  • Filipe Saraiva
  • Flavio Coelho
  • Flavio Percoco
  • Floris Bruynooghe
  • Frank Wierzbicki
  • François Dion
  • François Marier
  • Frederik Rietdijk
  • From Python Import Podcast
  • Full Stack Python
  • Gaël Varoquaux
  • Georges Dubus
  • Ghaandee on IT
  • Giampaolo Rodola
  • Giulio Fidente
  • Glenn Franxman
  • Glyph Lefkowitz
  • Go Deh
  • Gocept Weblog
  • Godson Gera
  • Graeme Cross
  • Graham Dumpleton
  • Graham Wheeler
  • Grant Baillie
  • Grant Rettke
  • Greg Taylor
  • Grig Gheorghiu
  • Grzegorz Śliwiński
  • Guido van Rossum
  • Gustavo Narea
  • Gustavo Niemeyer
  • Gökhan Sever
  • Hernan Grecco
  • Hilary Mason
  • Holger Krekel
  • Holger Peters
  • Humberto Rocha
  • Hynek Schlawack
  • Ian Ozsvald
  • Ilian Iliev
  • Import Python
  • Inspired Python
  • Ionel Cristian Maries
  • IronPython-URLs
  • IslandT
  • Israel Fruchter
  • Itamar Turner Trauring
  • ItsMyCode
  • Ivan Velichko
  • J. Pablo Fernández
  • Jack Diederich
  • Jacob Perkins
  • Jahongir Rahmonov
  • Jaime Buelta
  • Jamal Moir
  • James Bennett
  • Janusworx
  • Jarrod Millman
  • Jean-Paul Calderone
  • Jeff Bisbee
  • Jeff Bradberry
  • Jeff Hinrichs
  • Jeff Shell
  • Jeremy Epstein
  • Jeremy Hylton
  • Jim Fulton
  • Joe Abbate
  • Joe Pitz
  • Johan Dahlin
  • John Burns
  • John Cook
  • John Jacobsen
  • John Ludhi/nbshare.io
  • Jon Parise
  • Jonathan Ellis
  • Jonathan Harrington
  • Jonathan Hartley
  • Jonathan Street
  • Jorgen Schäfer
  • Juan Manuel Contreras
  • Julien Danjou
  • Julien Tayon
  • Juri Pakaste
  • Just a little Python
  • Justin Mayer
  • Kai Lautaportti
  • Karim Elghamrawy
  • Kay Hayen
  • Kay Schluehr
  • Kelly Yancey
  • Kodnito
  • Kogan Dev
  • Konrad Delong
  • Koodaamo
  • Kristján Valur Jónsson
  • Kriti Godey
  • Kulbir Saini
  • Kumar McMillan
  • Kumar Vipin Yadav
  • Kushal Das
  • Károly Nagy
  • LAAC Technology
  • Laurent Szyster
  • Leigh Honeywell
  • Lennart Regebro
  • Leonhard Vogt
  • Lintel Technologies
  • Linux Stans
  • ListenData
  • Logilab
  • Low Kian Seong
  • Luca Botti
  • Lucas Cimon
  • Ludovic Gasc
  • Ludvig Ericson
  • Luke Plant
  • Maciej Fijalkowsk
  • Made With Mu
  • Mahmoud Hashemi
  • Malthe Borch
  • Marc Richter
  • Marc-André Lemburg
  • Marcos Dione
  • Marius Gedminas
  • Mark Dufour
  • Mark McLoughlin
  • Mark McMahon
  • Martijn Pieters
  • Martin Fitzpatrick
  • Mats Kindahl
  • Matt Layman
  • Matthew Rocklin
  • Matthew Rollings
  • Matthew Wilson
  • Matthew Wright
  • Mattias Brändström
  • Mauveweb
  • Michael Becker
  • Michael Droettboom
  • Michael Foord
  • Michael Nelson
  • Michal Kwiatkowski
  • Michał Bultrowicz
  • Michele Simionato
  • Michy Alice
  • Mike C. Fletcher
  • Mike Driscoll
  • Mike Müller
  • Mikhail Korobov
  • Mikko Ohtamaa
  • Mirek Długosz
  • Mitchell Garnaat
  • Mitya Sirenef
  • Montreal Python User Group
  • Moshe Zadka
  • Moya Project
  • Mozilla Web Development
  • Muharem Hrnjadovic
  • Mycli
  • Nadav Samet
  • Naomi Ceder
  • Natan Zabkar
  • Ned Batchelder
  • Neil Schemenauer
  • Nick Coghlan
  • Nick Craig-Wood
  • Nick Efford
  • Nick Janetakis
  • Nicola Iarocci
  • Nicolas Dumazet
  • Nicolas Paris
  • Nigel Babu
  • Nikola
  • Nikolaos Diamantis
  • Not Invented Here
  • Nsukami Patrick
  • Obey the Testing Goat
  • Ofosos
  • Omaha Python Users Group
  • Ondřej Čertík
  • Paolo Amoroso
  • Paolo Melchiorre
  • Pathwright
  • Patrice Neff
  • Patrick Kennedy
  • Paul Everitt
  • Paul Harrison
  • Paul Redman
  • Paweł Fertyk
  • Pedro Lima
  • Peter Bengtsson
  • Peter Eisentraut
  • Peter Fankhänel
  • Peter Harkins
  • Peter Hoffmann
  • Phil Hassey
  • Philip Jenvey
  • Philipp von Weitershausen
  • Philippe Normand
  • Phillip J. Eby
  • Podcast.__init__
  • Polyglot.Ninja()
  • Possbility and Probability
  • Pradeep Gowda
  • Pranav Pandey
  • Praveen Gollakota
  • Programiz
  • Programming Ideas With Jake
  • Przemysław Kołodziejczyk
  • PyATL Bytecode
  • PyBites
  • PyCharm
  • PyCoder’s Weekly
  • PyCon
  • PyPy
  • PyTennessee
  • Python 4 Kids
  • Python 411 Podcast
  • Python Advocacy
  • Python Anywhere
  • Python Bytes
  • Python Celery - Weekly Celery Tutorials and How-tos
  • Python Circle
  • Python Data
  • Python Diary
  • Python Does What?!
  • Python Engineering at Microsoft
  • Python GUIs
  • Python Insider
  • Python Morsels
  • Python Piedmont Triad User Group
  • Python Pool
  • Python Software Foundation
  • Python Sweetness
  • Python User Groups
  • Python for Beginners
  • Python on Karan
  • Python with Myo
  • Python(x,y) News
  • PythonClub - A Brazilian collaborative blog about Python
  • PythonDebugging.com
  • Pythonicity
  • Pythonology
  • Python⇒Speed
  • Péter Szabó
  • Péter Zsoldos
  • Quansight Labs Blog
  • R David Murray
  • RMOTR
  • Ralph Bean
  • Ram Rachum
  • Randell Benavidez
  • Randle Taylor
  • Randy Zwitch
  • Raymond Hettinger
  • Read the Docs
  • Real Python
  • Red Hat Developers
  • Rene Dudfield
  • Reuven Lerner
  • Richard Tew
  • Richard Wall
  • Rickard Lindberg
  • Rob Galanakis
  • Robert Collins
  • Robert Zaremba
  • Robin Parmar
  • Robin Wilson
  • Rodrigo Araúj
  • RoseHosting Blog
  • Ruslan Spivak
  • Ryan Cox
  • S. Lott
  • S. R. Krishnan
  • SDJournal
  • SPE Weblog
  • STX Next
  • Salim Fadhley
  • Sandipan Dey
  • Sandro Tosi
  • Sean McGrath
  • Sebastian Pölsterl
  • Sebastian Witowski
  • Selena Deckelmann
  • Senthil Kumaran
  • Shannon -jj Behrens
  • ShiningPanda
  • Simeon Franklin
  • Simeon Visser
  • Simon
  • Simon Brunning
  • Simon Wittber
  • Simple is Better Than Complex
  • SoftFormance
  • Speed Matters
  • Spike ekipS
  • Spyder IDE
  • Stack Abuse
  • Stanislas Morbieu
  • Starzel.de
  • Stefan Behnel
  • Stefan Scherfke
  • Stein Magnus Jodal
  • Stephen Ferg
  • Steve Holden
  • Steven Klass
  • Stuart Gordon Reid
  • Stéphane Wirtel
  • Sumana Harihareswara - Cogito, Ergo Sumana
  • Suresh Dasari/Tutlane.com
  • Swisscom ICT
  • Talk Python to Me
  • Taylor Edmiston
  • TechBeamers Python
  • Techiediaries - Django
  • Terri Oda
  • Terry Jones
  • Test and Code
  • TestDriven.io
  • The Data Scientist
  • The Digital Cat
  • The No Title® Tech Blog
  • The Occasional Occurrence
  • The Open Sourcerer
  • The Parcon Blog
  • The Python Coding Blog
  • The Python Papers
  • The Three of Wands
  • Thibauld Nion
  • ThisHosting.Rocks
  • Thomas Guest
  • Thomas Vander Stichele
  • Tibo Beijen
  • Tim Arnold / reachtim
  • Tim Gilbert
  • Tim Knapp
  • Tim Lesher
  • Tobias Ivarsson
  • Tom Christie
  • Tomasz Ducin
  • Tomaž Muraus
  • Tomer Filiba
  • Tony Breyal
  • Toshio Kuratomi
  • Travis Oliphant
  • Trey Hunner
  • Tryton News
  • Turnkey Linux
  • Twisted Matrix Labs
  • V.S. Babu
  • Varun Nischal
  • Vasudev Ram
  • Vinay Sajip
  • Vinay Sajip (Logging)
  • Vinayak Mehta
  • Virgil Dupras
  • Vladimir Perić
  • Wayne Witzel
  • Weekly Python Chat
  • Weekly Python StackOverflow Report
  • Wes Mason
  • Wesley Chun
  • Will Kahn-Greene
  • Will McGugan
  • Will Pierce
  • William Minchin
  • William Reade
  • Wing Tips
  • Wingware
  • Wyatt Baldwin
  • Yaniv Aknin
  • Yann Larrivée
  • Yuval Greenfield
  • Zaki Akhmad
  • Zato Blog
  • Zero to Mastery
  • Zero-with-Dot (Oleg Żero)
  • ZeroDB
  • bottlepy-dev
  • codeboje
  • death and gravity
  • eGenix.com
  • hypothesis.works articles
  • kdev-python
  • meejah.ca
  • nl-project
  • pgcli
  • py.CheckIO
  • pythonwise
  • qutebrowser development blog
  • saaj/recollection
  • scikit-learn
  • testmon
  • tryexceptpass
  • Éric Araujo
  • Łukasz Langa
  • To request addition or removal:
    Open an issue on github