Pandas is the most popular python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python.
We can analyze data in pandas with:
- Series
- DataFrames
Series:
Series is one dimensional(1-D) array defined in pandas that can be used to store any data type.
Code #1: Creating Series
# Program to create series import pandas as pd # Import Panda Library # Create series with Data, and Index a = pd.Series(Data, index = Index) |
Here, Data can be:
- A Scalar value which can be integerValue, string
- A Python Dictionary which can be Key, Value pair
- A Ndarray
Note: Index by default is from 0, 1, 2, …(n-1) where n is length of data.
Code #2: When Data contains scalar values
# Program to Create series with scalar values Data =[1, 3, 4, 5, 6, 2, 9] # Numeric data # Creating series with default index values s = pd.Series(Data) # predefined index values Index =['a', 'b', 'c', 'd', 'e', 'f', 'g'] # Creating series with predefined index values si = pd.Series(Data, Index) |
Output:
Scalar Data with default Index
Scalar Data with Index
Code #3: When Data contains Dictionary
# Program to Create Dictionary series dictionary ={'a':1, 'b':2, 'c':3, 'd':4, 'e':5} # Creating series of Dictionary type sd = pd.Series(dictionary) |
Output:
Dictionary type data
Code #4:When Data contains Ndarray
# Program to Create ndarray series Data =[[2, 3, 4], [5, 6, 7]] # Defining 2darray # Creating series of 2darray snd = pd.Series(Data) |
Output:
Data as Ndarray
DataFrames:
DataFrames is two-dimensional(2-D) data structure defined in pandas which consists of rows and columns.
Code #1: Creation of DataFrame
# Program to Create DataFrame import pandas as pd # Import Library a = pd.DataFrame(Data) # Create DataFrame with Data |
Here, Data can be:
- One or more dictionaries
- One or more Series
- 2D-numpy Ndarray
Code #2: When Data is Dictionaries
# Program to Create Data Frame with two dictionaries dict1 ={'a':1, 'b':2, 'c':3, 'd':4} # Define Dictionary 1 dict2 ={'a':5, 'b':6, 'c':7, 'd':8, 'e':9} # Define Dictionary 2 Data = {'first':dict1, 'second':dict2} # Define Data with dict1 and dict2 df = pd.DataFrame(Data) # Create DataFrame |
Output:
DataFrame with two dictionaries
Code #3: When Data is Series
# Program to create Dataframe of three series import pandas as pd s1 = pd.Series([1, 3, 4, 5, 6, 2, 9]) # Define series 1 s2 = pd.Series([1.1, 3.5, 4.7, 5.8, 2.9, 9.3]) # Define series 2 s3 = pd.Series(['a', 'b', 'c', 'd', 'e']) # Define series 3 Data ={'first':s1, 'second':s2, 'third':s3} # Define Data dfseries = pd.DataFrame(Data) # Create DataFrame |
Output:
DataFrame with three series
Code #4: When Data is 2D-numpy ndarray
Note: One constraint has to be maintained while creating DataFrame of 2D arrays – Dimensions of 2D array must be same.
# Program to create DataFrame from 2D array import pandas as pd # Import Library d1 =[[2, 3, 4], [5, 6, 7]] # Define 2d array 1 d2 =[[2, 4, 8], [1, 3, 9]] # Define 2d array 2 Data ={'first': d1, 'second': d2} # Define Data df2d = pd.DataFrame(Data) # Create DataFrame |
Output:
DataFrame with 2d ndarray
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