Start your data science journey with Python. Learn practical Python programming skills for basic data manipulation and analysis.

- Home
- Python Essentials for Data Analysis I
- 1.1 Getting started - Hello, World!
- 1.2 Variables
- 1.3 Data types
- 1.4 Printing
- 1.5 Lists
- 1.6 Dictionaries
- 1.7 Input function
- 1.8 Arithmetic operators
- 1.9 Comparison operators
- 1.10 Logical operators
- 1.11 Identity operators
- 1.12 Membership operators
- 1.13 Conditional statements (if-elif-else)
- 1.14 Importing modules
- 1.15 For loops
- 1.16 While loops

- Python Essentials for Data Analysis IIToggle Dropdown
- 2.1 Introduction to Functions in Python
- 2.2 Functions - Arguments
- 2.3 Functions with Return Values
- 2.4 Functions - A Fun Exercise!
- 2.5 Functions - Arbitrary Arguments (*args)
- 2.6 Functions - Arbitrary Keyword Arguments (**kwargs)
- 2.7 Recursive Functions
- 2.8 Lambda Expressions
- 2.9 Functions - More Exercises

- Data Analysis with PandasToggle Dropdown
- PD.1 Introduction to Pandas
- PD.2 Basics of Pandas
- PD.3 Finding and Describing data
- PD.4 Assigning Data
- PD.5 Manipulating Data
- PD.6 Handling Missing Data
- PD.7 Removing and adding data
- PD.8 Renaming data
- PD.9 Combining data
- PD.10 Using Pandas with other functions/mods
- PD.11 Data classification and summary
- PD.12 Data visualisation

- Data Analysis with NumPyToggle Dropdown
- NP.1 Introduction to NumPy
- NP.2 Create Arrays Using lists
- NP.3 Creating Arrays with NumPy Functions
- NP.4 Array Slicing
- NP.5 Array Reshaping
- NP.6 Math with NumPy I
- NP.7 Combining 2 arrays
- NP.8 Adding elements to arrays
- NP.9 Inserting elements into arrays
- NP.10 Deleting elements from arrays
- NP.11 Finding unique elements and sorting
- NP.12 Math with NumPy II
- NP.13 Analysing data across arrays
- NP.14 NumPy Exercises

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Data types are the classification of data items. Data types represents a kind of value which determines what can be done to that data.

**What are the different types of data in Python?**

Data Types | Examples | Explanation | Mutable/Immutable? |
---|---|---|---|

Strings | "Hello!", "23.34" | Text - anything between" " becomes string |
Immutable |

Integers | 5364 | Whole numbers | Immutable |

Floats | 3.1415 | Decimal Numbers | Immutable |

Booleans | True, False | Truth values that represent Yes/No | Immutable |

Lists | [1,2,3,4,5] | A collection of data, sits between [ ] |
Mutable |

Tuples | (1,2,3,4,5) | A collection of data, sits between ( ) |
Immutable |

Dictionaries | {"a":1, "b":2, "c":3} | A collection of data, sits between { } |
Mutable |

Data types are set when you assign a value to a variable.

Examples | Type | Explanation |
---|---|---|

ex_1 = "Hello World" | string | The data assigned sits in between " " |

ex_2 = 254 | integer | The data assigned does not sit in between " " , and is a whole number |

ex_3 = 25.43 | float | The data assigned does not sit in between " " , and is a decimal number |

ex_4 = ["Anna", "Bella", "Cora"] | list | A list of strings - Data assigned sits in between " " , within [ ] |

Use the ** type( ) ** function to check data types.

** type(x) ** determines and returns what is the type of the input **x**

**Click the triangle button to run the codes and see the output:**

To convert variables from one type to another (i.e. integers to floats), we use *type conversions* as follows:

Data Type | Syntax |
---|---|

strings | str() |

integer | int() |

floats | float() |

lists | list() |

**Application**

- Last Updated: Jun 24, 2024 9:14 AM
- URL: https://libguides.ntu.edu.sg/python
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