Pandas - Series
A Pandas Series is one of the basic data structures in the Pandas library used in Python for data analysis. It represents a one-dimensional labeled array that can store different types of data such as numbers, strings, or objects.
- One-dimensional structure: A Series is like a single column in a table or a list with labels.
- Has an index: Each value has an index (label) that identifies it.
- Can store different data types: It can contain integers, floats, strings, or objects.
- Part of the Pandas library: It is widely used for data analysis, statistics, and machine learning preprocessing.
Common operations on a Pandas Series include indexing, slicing, mathematical calculations, filtering data, statistical functions, condition checking, and updating values.
- Accessing Elements (Indexing):
s = pd.Series([10, 20, 30, 40])
print(s[0]) # access first element - Slicing: print(s[1:3])
- Mathematical Operations: print(s + 5)
- Filtering Data: print(s[s > 20])
- Statistical Operations:
print(s.mean()) # average print(s.sum()) # sum print(s.max()) # maximum print(s.min()) # minimum
- Checking Conditions: print(s > 25)
- Adding or Updating Values:
s[2] = 100 s[4] = 50 print(s)
import pandas as pd
if __name__ == '__main__':
a = [3,7,10,44,55,22,23,18,99,91]
mySeries = pd.Series(a)
print("Series: ",mySeries)
print("Type of Series: ",type(mySeries))
print("Series Index: ",mySeries.index)
print("First Element of Series: ",mySeries[0])
print("Mean of Series: ",mySeries.mean()) # average
print("Sum of Series elements: ",mySeries.sum()) # sum
print("Maximum of Series: ",mySeries.max()) # maximum
print("Minimum of Series: ",mySeries.min()) # minimum
print("Slicing Series: ")
print(mySeries[3:7])
print("Filtering Series: ")
print("Values greater than 50\n",mySeries[mySeries>50])
print("Performing arithmetic operations: ")
print(mySeries+40)
fruits = ["apple", "banana", "cherry"]
s = pd.Series(["apple", "banana", "cherry"], index = ["one","two","three"])
print(s)
print("One: ",s["one"])
print("Two: ",s["two"])
running = {"day1": 4200, "day2": 3800, "day3": 3900}
myvar =pd.Series(running)
print(myvar)
print(f'running: {myvar["day1"]} steps')
print(s == "apple")