# FAQ: Creating, Loading, and Selecting Data with Pandas - Importing the Pandas Module

This community-built FAQ covers the “Importing the Pandas Module” exercise from the lesson “Creating, Loading, and Selecting Data with Pandas”.

Paths and Courses
This exercise can be found in the following Codecademy content:

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How do you make the Panda work on the Jupyter Notebook?

I have just completed regression modelling : Reggie linear regression.

Everything was fine and somehow i got to the answer . However in solution , I couldn’t comprehend what is the use of this smallest error = float(“inf”) in last code of the project.

datapoints = [(1, 2), (2, 0), (3, 4), (4, 4), (5, 3)]
smallest_error = float(“inf”)
best_m = 0
best_b = 0

for m in possible_ms:
for b in possible_bs:
error = calculate_all_error(m, b, datapoints)
if error < smallest_error:
best_m = m
best_b = b
smallest_error = error

print(best_m, best_b, smallest_error)

Hello everyone, I hope someone can help me
I’m iterating throgh a column of a dataframe, and trying to add the rows that match a certain condition to a new dataframe, but im having trouble adding all the rows, since everytime a new row matches the condition, it replaces the previous row, instead of being added to the dataframe, im trying to make the x=x+1 version of a dataframe basically
can anyone hel me?

Hi, would somebody help me with this code? I don’t understand why A column was returned with four 1.0 instead of 1.0, NaN, NaN, NaN?

import pandas as pd import numpy as np df = pd.DataFrame( { 'A': 1.0, 'B': np.array([3]*4, dtype= 'int32') } ) print(df)