Series Series are one-dimensional (1-D) arrays defined in Panda that can be used to store any data type.

Code 1: Authoring series

# Program to create series

# Import Panda Library
import pandas as pd  

# Create series with Data, and Index
a = pd.Series(Data, index = Index) 
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1. Scalar values can be integer values, strings
2. Python dictionaries can be key, value pairs
3. Ndarray

The index and value of a Series can be obtained through the two properties of the Series, index and value, respectively

obj.index
Out[5]: RangeIndex(start=0, stop=4, step=1)

obj.values
Out[7]: array([2, 9, 5, 6], dtype=int64)
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Code 2: When data contains scalar values

# Program to Create series with scalar values  

# Numeric data 
Data =[1, 3, 4, 5, 6, 2, 9]   

# 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)  
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Output:

Code 3: When data contains a 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)  
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Output:

Code 4: When data contains Ndarray

# Program to Create ndarray series 

# Defining 2darray 
Data =[[2, 3, 4], [5, 6, 7]]   

# Creating series of 2darray 
snd = pd.Series(Data)     
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Output:

The name of the Series index and the name of the value (equivalent to the names of the two vectors)

Obj3. name="population" obj3.index. Name ="ind" obj3 Out[23]: ind b 2.0 a 1.0 d NaN name: population, dtype: float64Copy the code

DataFrames:

DataFrames are two-dimensional (2-D) data structures consisting of rows and columns defined in panda. Is a typical tabular data, both row index and column index. Equivalent to a large dictionary whose key is a column index and whose value is a Series; The Series that make up each of these indexes share a Series index.

Code 1:

# Program to Create DataFrame

# Import Library
import pandas as pd   

# Create DataFrame with Data
a = pd.DataFrame(Data)  
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Here, the data could be:

1. One or more

   * * * * dictionary

2. One or more

   * * * * series
3. **2D-Numpy Ndarray**

Code 2: When data is a dictionary

# Program to Create Data Frame with two dictionaries

# Define Dictionary 1
dict1 ={'a':1, 'b':2, 'c':3, 'd':4}   

# Define Dictionary 2     
dict2 ={'a':5, 'b':6, 'c':7, 'd':8, 'e':9} 

# Define Data with dict1 and dict2
Data = {'first':dict1, 'second':dict2} 

# Create DataFrame 
df = pd.DataFrame(Data)  
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Output:

DataFrame takes a series by default by column index; (In series, a value is obtained by index by default)

Df ["popular"] or df. Popular Out[37]: 0 8 1 9 2 10 3 11 Name: dtype: int64Copy the code

Code 3: When data is a sequence

# Program to create Dataframe of three series import pandas as pd # Define series 1 s1 = pd.Series([1, 3, 4, 5, 6, 2, 5]) # Define s2 = pd.series (['a', 'b', 'c', 'd', 'e']) # Define Data Data ={'first':s1, 'second':s2, 'third':s3} # Create DataFrame dfseries = pd.DataFrame(Data)Copy the code

Output:

The DataFrame indirectly acquires the row vector via IX, which is also a series whose index is the column index of the original DF.

df.loc[2]
Out[40]: 
cities       bj
year       2003
popular      10
Name: 2, dtype: object
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Note: When creating dataframes for 2D arrays, one constraint must be maintained — the dimensions of the 2D arrays must be the same.

# Program to create DataFrame from 2D array

# Import Library
import pandas as pd 

# Define 2d array 1
d1 =[[2, 3, 4], [5, 6, 7]] 

# Define 2d array 2
d2 =[[2, 4, 8], [1, 3, 9]] 

# Define Data
Data ={'first': d1, 'second': d2}  

# Create DataFrame
df2d = pd.DataFrame(Data)    
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Output: