I have loaded the below CSV file containing code and coefficient data into the below dataframe df:
CODE|COEFFICIENT
A|0.5
B|0.4
C|0.3
import pandas as pd
import numpy as np
df= pd.read_csv('cod_coeff.csv', delimiter='|', encoding="utf-8-sig")
giving
ITEM COEFFICIENT
0 A 0.5
1 B 0.4
2 C 0.3
From the above dataframe, I need to create a final dataframe as below which has a matrix structure with the product of the coefficients:
A B C
A 0.25 0.2 0.15
B 0.2 0.16 0.12
C 0.15 0.12 0.09
I am using np.multiply
but I am not successful in producing the result.
To create correlation matrix using pandas, these steps should be taken: 1 Obtain the data. 2 Create the DataFrame using Pandas. 3 Create correaltion matrix using Pandas Example 1: import pandas as pd data = {'A': [45, 37, 42], 'B': [38, 31, 26], 'C': [10, 15, 17] } df = pd.
You can use the scatter_matrix () function to create a scatter matrix from a pandas DataFrame: The following examples show how to use this syntax in practice with the following pandas DataFrame:
In the real world, a Pandas DataFrame will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, and Excel file. Pandas DataFrame can be created from the lists, dictionary, and from a list of dictionary etc. Dataframe can be created in different ways here are some ways by which we create a dataframe:
It offers a tool for cleaning and processes your data. It is the most popular Python library that is used for data analysis. In this article, We are going to learn about Pandas Data structure. It supports two data structures:
numpy as a faster alternative
pd.DataFrame(np.outer(df, df), df.index, df.index)
Given sample
30,000 rows
df = pd.concat([df for _ in range(10000)], ignore_index=True)
You want to do the math between a vector and its tranposition. Transpose with .T
and apply the matrix dot
function between the two dataframes.
df = df.set_index('CODE')
df.T
Out[10]:
CODE A B C
COEFFICIENT 0.5 0.4 0.3
df.dot(df.T)
Out[11]:
CODE A B C
CODE
A 0.25 0.20 0.15
B 0.20 0.16 0.12
C 0.15 0.12 0.09
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