hoffm386

// this is the background code...

// listen for our browerAction to be clicked
chrome.browserAction.onClicked.addListener(function (tab) {
	// for the current tab, inject the "inject.js" file & execute it
	chrome.tabs.executeScript(tab.ib, {
		file: 'inject.js'
	});
});

hoffm386

params[:id]

hoffm386

{
  "install": "pip3 install --user -r pip-requirements.txt",
  "start": "PYTHONUNBUFFERED=true python3 app.py",
  "watch": {
    "ignore": [
      "\\.pyc$"
    ],
    "install": {
      "include": [
        "pip-requirements.txt"

hoffm386

# Step 0: import relevant packages
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression

# Step 1: load all data into X and y
antelope_df = pd.read_csv("antelope.csv")
X = antelope_df.drop("spring_fawn_count", axis=1)

hoffm386

# Step 0: import relevant packages
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression

# Step 1: load all data into X and y

hoffm386

# Step 0: import relevant packages
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import LinearRegression

hoffm386

# Step 0: import relevant packages
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import LinearRegression