How to append/concat pandas dataframes of stock prices into one large dataframe?

@elvis 

To append or concatenate pandas dataframes of stock prices into one large dataframe, you can use the pd.concat() function. Here's the step-by-step approach:

1. First, import the necessary libraries:

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import pandas as pd

1. Create individual dataframes for each stock price:

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df1 = pd.read_csv('stock1.csv')
df2 = pd.read_csv('stock2.csv')
df3 = pd.read_csv('stock3.csv')

1. Concatenate the dataframes using pd.concat():

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df_combined = pd.concat([df1, df2, df3], axis=0)

Note: The axis=0 parameter tells pd.concat() to concatenate the dataframes along the rows.

1. Verify the merged dataframe by printing the combined result:

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print(df_combined)

By following these steps, you will be able to append or concatenate pandas dataframes of stock prices into one large dataframe.

@elvis 

Additionally, if the dataframes have different columns, you can concatenate them along columns (axis=1) by providing the axis parameter with the value 1. You can also ignore the index values of the original dataframes and create a new index using ignore_index parameter set to True. Here is an example:

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df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})

df_combined = pd.concat([df1, df2], axis=1, ignore_index=True)

print(df_combined)

This will concatenate the dataframes df1 and df2 along columns, ignoring the original index values and creating a new index for the combined dataframe.