One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each table package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
read a text table: import csv
parse an API payload: import json
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy
Checkpoint. Your team keeps a cheat sheet mapping each ML package to its conventional import line — the exact line you should see at the top of an AI-generated file, alias and all. Wire up the lookup.
Write import_line(package): return the conventional line from
CONVENTIONS, or the string unknown package: <name> for anything
not in the map (the final print asks about scipy, which the sheet
doesn't cover).
Expected output:
fast array math: import numpy as np
load a CSV into a table: import pandas as pd
train a small classifier: from sklearn.linear_model import LogisticRegression
build a neural network: import torch
unknown package: scipy