How do I convert rows into a calendar performance table using Python/Pandas or Excel?-CodePudding

I have 2 columns which hold dates and performance returns by month back several years. The data is in a csv and looks like this:

Date: Return

12/2022: -1%

11/2022:  2%

10/2022:  1%

...

1/2002: -1%

I want to convert this into a more traditional performance table/matrix which has a row for each year and a column for each month. So, the end result would look something like this:

Year     Jan, Feb, Mar, ..., Dec

2022 -1%,  1%,  2%, ..., -1%

2021 -1%,  1%,  2%, ..., -1%

...

2002 -1%,  1%,  2%, ..., -1%

Please advise.

Thx

CodePudding user response：

To convert a row into a calendar performance table using Python/Pandas, you can use the following steps:

First, import the required libraries: Copy code import pandas as pd Load the data into a Pandas DataFrame. For example, if your data is in a CSV file named "data.csv", you can use the following code to load it into a DataFrame: Copy code df = pd.read_csv("data.csv") Select the row that you want to convert into a calendar performance table. For example, if you want to select the first row, you can use the following code: Copy code row = df.iloc[0] Create a new DataFrame for the calendar performance table, with columns for the date, the performance value, and any other relevant data. For example: Copy code calendar_df = pd.DataFrame(columns=['date', 'performance']) Iterate through the elements in the selected row, and add a row to the calendar DataFrame for each element. For example: Copy code for i, value in enumerate(row): calendar_df = calendar_df.append({'date': i, 'performance': value}, ignore_index=True) If your data includes dates rather than numerical indices, you can use the date column to set the index of the calendar DataFrame, and then use the unstack method to convert the calendar DataFrame into a table with one column per date: Copy code calendar_df.set_index('date', inplace=True) calendar_table = calendar_df.unstack() To convert a row into a calendar performance table using Excel, you can use the following steps:

Open the spreadsheet containing the data in Excel.

Select the row that you want to convert into a calendar performance table.

Click the "Insert" tab, and then click the "Table" button in the Tables group.

In the Create Table dialog box, make sure that the range of cells containing the data is selected, and then click "OK".

Excel will create a table with one row for each element in the selected row, and one column for the date and one column for the performance value.

If your data includes dates rather than numerical indices, you can use the "Design" tab to change the name of the first column to "Date", and then use the "PivotTable" button in the Tables group to create a pivot table with one column per date.

CodePudding user response：

You can use pandas like so:

# simulate csv file // only for copy paste example required
from io import StringIO
csv = StringIO(
"""Date:Return
12/2022:-1%
11/2022: 2%
10/2022: 1%"""
)

actual code:

# load tabular data, use filepath instead of 'csv' to load from disk
import pandas as pd
df = pd.read_csv(csv, sep=':')

# str date to datetime object to access year, month, etc.
df['Date'] = pd.to_datetime(df['Date'])
# string to float
df['Return'] = df['Return'].str.strip('%').astype(float) /100

# aggregate data as pivot table
import numpy as np
out = pd.pivot_table(df, values='Return', index=df['Date'].dt.year, columns=df['Date'].dt.month, aggfunc=np.sum)
print(out)

Out:

Date    10    11    12
Date                  
2022  0.01  0.02 -0.01

CodePudding user response：

here is one way :

df[['year','month']] = pd.to_datetime(df['date']).dt.strftime('%Y-%b').str.split('-', expand=True)
df = df.pivot(index='year', columns=['month'], values=['return'])

output :

month    Dec  Jan  Nov  Oct
year                       
2002     NaN  -2%  NaN  NaN
2022     -1%  NaN   1%   0%