title : Introduction to Python for Data Analysis description : This chapter will get you started with Python for Data Analysis. We will cover the reasons to learn Data Science using Python, provide an overview of the Python ecosystem and get you to write your first code in Python!
--- type:MultipleChoiceExercise lang:python xp:50 skills:2 key:9a8fd577a9
Python (an interpreted language) has gathered a lot of interest recently as a preferred choice of language for data analysis. Here are some reasons in favour of learning Python:
- It is open source – free to install and use
- Python has an awesome online community - latest algorithms come to Python in a matter of days
- It is easy to learn
- It can become a common language for data science and production of web-based analytics products
####Which of the following is not a reason to learn Python for data analysis?
*** =instructions
- Python is easy to learn.
- Python is an interpreted language, so computation times can be higher than compiler based languages in some cases.
- Python has good libraries for data science.
- It is a production ready language (from web & software perspective).
*** =hint Interpreted languages are typically easier to learn, but take longer computational time than compiler based languages.
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package
msg_bad1 = "That is a good reason to learn Python! Think again"
msg_success = "Exactly! Since Python is an interpreted language, the computation times can be higher compared to other compiler based languages."
# Use test_mc() to grade multiple choice exercises.
# Pass the correct option (Action, option 2 in the instructions) to correct.
# Pass the feedback messages, both positive and negative, to feedback_msgs in the appropriate order.
test_mc(2, [msg_bad1, msg_success, msg_bad1, msg_bad1])
--- type:MultipleChoiceExercise lang:python xp:50 skills:2 key:db5fe12eff
You will come across this question soon after you start using Python. Python has 2 popular competing versions. Both versions have their pros and cons.
Benefits of Python 2.7
- Awesome online community. Easier to find answers when you get stuck at places.
- Tonnes of third party libraries
Benefits of Python 3.5
- Cleaner and faster
- It is the future!
You can read a more detailed answer here
####Which version of Python would you recommend to someone who needs to use several third party libraries?
*** =instructions
- Python 2.7
- Python 3.5
- Should work on both
*** =hint If you need several third party tools, you should look for a version which has higher community support and integrations.
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package
msg_bad1 = "Python 3.5 is newer and has lesser third party packages compared to Python 2.7"
msg_success = "Python 2.7 has much higher compatibility with third party libraries."
msg_bad2 = "Think again! One of them is better than the other in this scenario"
# Use test_mc() to grade multiple choice exercises.
# Pass the correct option (Action, option 2 in the instructions) to correct.
# Pass the feedback messages, both positive and negative, to feedback_msgs in the appropriate order.
test_mc(1, [msg_success, msg_bad1, msg_bad2])
--- type:MultipleChoiceExercise lang:python xp:50 skills:2 key:2f83694db6
While DataCamp provides an awesome interface to get you started, you will need to run a local instance of Python for any serious Data Science work. The simplest way would be to download Anaconda. An open source distribution of Python, it has most of the libraries & packages you would need, and removes any version conflicts. I strongly recommend this for beginners. For this course, we will be using Python 3.x
####Should you install a local instance of Python on your machine to continue this course?
*** =instructions
- Yes
- No
- I need some help
*** =hint Download Anaconda
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package
msg_bad = "You should install a Python instance locally before going forward"
msg_success = "Great! You are all set to go ahead"
msg_help = "Drop us a line at [email protected]"
# Use test_mc() to grade multiple choice exercises.
# Pass the correct option (Action, option 2 in the instructions) to correct.
# Pass the feedback messages, both positive and negative, to feedback_msgs in the appropriate order.
test_mc(1, [msg_success, msg_bad, msg_help])
--- type:NormalExercise lang:python xp:100 skills:2 key:af2f6f90f3
Time to get our hands dirty now. We will use Python to run a simple program!
*** =instructions
- The first line adds two numbers (1 & 2) and stores it in variable addition1.
- Write a line of code in line 4, which adds the number 3 and the number 4 and assigns it to a variable addition2
*** =hint
- Think how would you write simple addition.
- Make sure you assign the sum to the variable 'addition2'
- Remember - Python is case sensitive. Check your cases and white spaces
*** =pre_exercise_code
# The pre-exercise code runs code to initialize the user's workspace. You can use it for several things:
*** =sample_code
# Add 1 & 2 and assign it to addition1
addition1 = 1 + 2
# Now write code to add 3 & 4 and assign it to addition2
*** =solution
# Add 1 & 2 and assign it to addition1
addition1 = 1 + 2
# Now write code to add 3 & 4 and assign to addition2
addition2 = 3 + 4
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package. Documentation can also be found at github.com/datacamp/pythonwhat/wiki
# Check if the student typed 3 + 4
test_object("addition2")
success_msg("Great work! Let's print something now!")
--- type:NormalExercise lang:python xp:100 skills:2 key:b52d6e84c1
Now that you know how to add numbers, let us look at printing "Hello World!" in Python.
*** =instructions
- Print "Hello World!" on the console
*** =hint
- Remember that the message to be printed should be enclosed in (" ")
- Remember - Python is case sensitive. Check your cases and white spaces
- Hope you are not missing the exclaimation mark !
*** =pre_exercise_code
# The pre-exercise code runs code to initialize the user's workspace. You can use it for several things:
*** =sample_code
# Print a message
print("Welcome to the joint course from Analytics Vidhya and DataCamp")
# Now write code to print "Hello World!"
*** =solution
# Print a message
print("Welcome to the joint course from Analytics Vidhya and DataCamp")
# Now write a code to Print "Hello World!"
print("Hello World!")
*** =sct
# The sct section defines the Submission Correctness Tests (SCTs) used to
# evaluate the student's response. All functions used here are defined in the
# pythonwhat Python package. Documentation can also be found at github.com/datacamp/pythonwhat/wiki
# Check if the student printed "Hello World!"
test_output_contains("Hello World!", pattern = False, no_output_msg="Did you print Hello World! ?")
success_msg("Great work! Let's move to the next chapter")