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Ethereum: Regarding addition of Index with loops in same column in Python Numpy and Pandas

13 February، 2025 CRYPTOCURRENCY

Here is an article on how to add rows to a column in the same column using loops with Numpy and Pandas:

Adding Rows to a Column in the Same Column

When working with cryptocurrency data from Binance, it can be tricky to handle large data sets. A common problem is when we need to perform automated operations on the data, such as adding new features or rows to existing columns.

In this article, we will explore how to add rows to a column in the same column using Numpy and Pandas.

Why Rows or Columns?

Before we dive into the solution, let’s quickly discuss why we work with rows instead of columns. In most cases, cryptocurrency data is stored in a one-dimensional array (e.g. NumPy), where each row represents a single observation or sample. Adding a new column to this array can be as simple as adding a new element to the end of the array.

Solution: Using Loops

However, when working with large data sets, we may need to perform operations on all rows in a specific column. In such cases, using loops is an efficient way to add new rows to the same column.

Here is a step-by-step solution:

import pandas as pd
import numpy as np







Ethereum: Regarding addition of Index with loops in same column in Python Numpy and Pandas


Convert data from Binance to Pandas DataFrame and NumPy array
df = pd.DataFrame({'Price': [100, 200, 300]}) 
replace with your data
array = np.array([1, 2, 3]) 
replace with your data

Define column index (0-based)
col_index = 0

Initialize an empty list to store new rows
new_rows = []

Loop through each row in the DataFrame (or array)
for i, value in enumerate(df[col_index]):

Add a new row to the list
new_row = {
'Price': df[col_index][i] + np.random.uniform(-0.1, 0.1) 
add some randomness for demonstration purposes
}
new_rows.append(new_row)

Concatenate the new rows with the original DataFrame (or array)
df.loc[:, col_index] = pd.concat(new_rows, ignore_index=True).tolist()

In this solution:

  • We loop through each row in the specified column using enumerate.
  • For each row, we add a new row to the new_rows list.
  • We use Pandas’ concat() function to concatenate the new rows with the original DataFrame (or array) and ignore the index.

Note: The above solution assumes that the data is stored in a one-dimensional array (e.g. NumPy). If your data is stored in a different format, you may need to modify the solution accordingly.

By using loops to add rows to the same column, we can efficiently handle large data sets and perform automated operations on Binance cryptocurrency data.

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