Python Basics — Variables, Data Types & Print

int, float, str, bool, list, tuple, dict, set

For data analytics, you will mostly work with int and float (numbers), str (text/column names), bool (True/False filters), list and dict (structured data). Know which is mutable and which is not.

x = 10      # int — whole number
y = 10.5    # float — decimal number
type(x)     # <class "int">
type(y)     # <class "float">

When you read a CSV with Pandas, numeric columns with decimals become float64, whole numbers become int64. Knowing this helps you debug dtype errors.

x = 42
print(type(x))   # <class "int">

df["salary"].dtype   # in Pandas — dtype("int64")

type() is for plain Python objects. In Pandas, use .dtype on a Series or .dtypes on a DataFrame to check column types before transformations.

x = 10        # assignment — stores value in variable
x == 10       # comparison — returns True or False

Using = when you meant == inside a condition causes a SyntaxError in Python (unlike some languages). This catches beginners. In Pandas: df[df["age"] == 25] — always ==.

s = "123"
n = int(s)    # 123
f = float(s)  # 123.0

Type conversion is common when reading data from CSVs or APIs where numbers arrive as strings. Pandas usually handles this automatically, but manual conversion is needed for edge cases.

name = "Priya"
age = 25
print(f"Name: {name}, Age: {age}")   # f-string (preferred)
print("Name: {}, Age: {}".format(name, age))   # .format()

f-strings (Python 3.6+) are the cleanest way to format output. In data analysis you use them for logging results, printing summaries, and building dynamic report messages.