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I am trying to create a pandas dataframe that looks like this:

 -5 0 5 index -5 NaN slope slope 0 slope NaN slope 5 slope slope NaN

but the closest I can get is the code below which returns a dataframe with only one column (which is the list from the last iteration through the ctr1 loop)

weather = np.linspace(-5, 5, 3) for ctr1 in weather: slope_list = [] X1 = round(ctr1,1) for ctr2 in weather: X2 = round(ctr2,1) Y1 = regressor[0] * X1**3 + \ regressor[1] * X1**2 + \ regressor[2] * X1 + \ regressor[3] Y2 = regressor[0] * X2**3 + \ regressor[1] * X2**2 + \ regressor[2] * X2 + \ regressor[3] slope = (Y2-Y1)/(X2-X1) slope_list.append(slope) df_final = pd.DataFrame({X1:slope_list})

Can anyone help?

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  • Please provide expected output. Commented Oct 13, 2018 at 9:37
  • Hi andrew_reece, the expected output is as I have shown right at the top where each 'slope' is a number (based on the calculation using X1, X2, Y1, Y2). Commented Oct 13, 2018 at 9:52

4 Answers 4

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df_final is getting only 3 elements because it's at the same indentation level as for ctr2 in weather, so it's getting reassigned every outer loop. Although, if you fix that, you'll get a dataframe that's only a single long column: you only have a single slope_list getting appended to that turns into a dataframe at the end.

This is how I would solve that without changing your assignment method:

weather = np.linspace(-5, 5, 3) slope_list = [] for ctr1 in weather: X1 = round(ctr1,1) for ctr2 in weather: X2 = round(ctr2,1) Y1 = regressor[0] * X1**3 + \ regressor[1] * X1**2 + \ regressor[2] * X1 + \ regressor[3] Y2 = regressor[0] * X2**3 + \ regressor[1] * X2**2 + \ regressor[2] * X2 + \ regressor[3] slope = (Y2-Y1)/(X2-X1) slope_list.append(slope) #make it 3 columns and 3 rows as intended slope_list = np.array(slope_list).reshape(3, 3) #make the dataframe df_final = pd.DataFrame({X1:slope_list}) #manually add the desired row and column indexes df_final = df.set_index(weather) df_final.columns = weather

Although you should keep in mind that unless you know exactly what you're doing, making loops and nested loops when working with pandas usually means you're missing a much easier and better way to go about things.

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You can try directly assign values in DataFrame. Just create empty DataFrame with index=weather:

import numpy as np weather = np.linspace(-5, 5, 3) df_final = pd.DataFrame([], index=weather) for ctr1 in weather: X1 = round(ctr1,1) for ctr2 in weather: X2 = round(ctr2,1) Y1 = regressor[0] * X1**3 + \ regressor[1] * X1**2 + \ regressor[2] * X1 + \ regressor[3] Y2 = regressor[0] * X2**3 + \ regressor[1] * X2**2 + \ regressor[2] * X2 + \ regressor[3] slope = (Y2-Y1)/(X2-X1) df_final.loc[X1, X2] = np.NaN if X1 == X2 else slope
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For integer positional setting, I recommend you use the more efficient .iat instead of .loc.
Hi Alexey, thank you for your solution. For completeness, I thought I would provide the only update I needed to add for it to work perfectly for a larger weather array. I have added my own solution based mostly on your code.
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slope_list = [] resets resulting list at each iteration, so just the last one remains. You need to define result list outside the outer loop, and append subresults to it.

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Thanks Kosist, the reason I reset slope_list inside the outer loop is because I only want each column to have 3 rows. If I set it outside the outer loop, I end up with a list with 9 values.
Could you post regressor data also (better then full code), so we could try to run the code? B/c now you reset data by this, and that's why there is just one column with the last ctr1 iteration.
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As mentioned, for completeness, I have posted an answer to my question that works with a larger weather array. The only difference is that I did the rounding earlier in the code:

weather = np.round(np.linspace(-5, 35, 401), decimals = 1) df_final = pd.DataFrame([], index=weather) for ctr1 in weather: X1 = ctr1 for ctr2 in weather: X2 = ctr2 Y1 = regressor[0] * X1**3 + \ regressor[1] * X1**2 + \ regressor[2] * X1 + \ regressor[3] Y2 = regressor[0] * X2**3 + \ regressor[1] * X2**2 + \ regressor[2] * X2 + \ regressor[3] slope = (Y2-Y1)/(X2-X1) df_final.loc[X1, X2] = np.NaN if X1 == X2 else slope
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