Ethereum: About adding a looping index on the same column in Python Numpy and Pandas

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 difficult to process 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 Instead of Columns?

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

Solution: Use loops

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

Here is a step-by-step solution:

import pandas as pd

import numpy as np


Convert Binance data 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 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]):


Append 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 iterate over 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), ignoring 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 adapt the solution accordingly.

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

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