Welcome to the very first issue of Pandas Daily — your 5 minute daily dose of Python.

Today: Understand some of Python’s data types with examples and clean output.

“Data Types” help the computer understand what you're working with—numbers, text, or something else.

Common Data Types

1. Numbers

Reminder: As seen below, "In [1]" is the code input we will type in and "Out [1]" is the output of code once executed

  In [1]: # Integers (anything starting with '#' is a comment and not executed)

  8

  Out [1]: 8

  In [2]: # Floating numbers(decimal ones)

  4.22

  Out [2]: 4.22

✅ To check in case I am lying - use "type" command

  In [3]:
  type(8)

  Out [3]: int

  In [4]:
  type(4.22)

  Out [4]: float

📚📈 What "Math" you can do with numbers

  In [5]: # Addition (or subtraction)

  8 + 4.22

  Out [5]: 12.22

  In [6]: # Multiplication

  8 * 4.22

  Out [6]: 33.76

  In [7]: # Division

  8 / 4

  Out [7]: 2.0

  In [8]: # Exponents (2 to the power of 3 -> 2 X 2 2 X 2 = 8)

  2 ** 3

  Out [8]: 8

♾️ Convert int to float and vice versa

  In [9]:
  int(2.8)

  Out [9]: 2

  In [10]:
  float(2)

  Out [10]: 2.0

2. Booleans (don't worry, just a fancy name) - True, False basically

  In [11]:
  True

  Out [11]: True

  In [12]:
  False

  Out [12]: False

🤜🤛Again use "type" to check in case I am lying about bools. Yes "bool" is for "booleans".

  In [13]:
  type(True)

  Out [13]: bool

🚨📝 They are primarily used to check some conditions or do comparisons

  In [14]: # Is 12 greater than 0?

  12 > 0

  Out [14]: True

  In [15]: # Is 12 less than 5?

  12 < 5

  Out [15]: False

💡Why the below error?

  In [16]:TRUE

  Out [16]:

    NameError: name 'TRUE' is not defined

😭Because Python is (CASE) sensitive

🧠🚀 "True" is also considered as 1 and False as 0. See below:

  In [17]:
  True + True

  Out [17]: 2

  In [18]:
  True + False

  Out [18]: 1

  In [19]:
  True + 1

  Out [19]: 2

Coming up tomorrow and beyond: Strings, lists, dictionaries - all keeping it simple!!

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