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”.

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This exercise can be found in the following Codecademy content:

Data Science

Data Analysis with Pandas

FAQs on the exercise Importing the Pandas Module

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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)