Train-test Split of a CSV file in Python

问题

I have a .csv file that contains my data. I would like to do Logistic Regression, Naive Bayes and Decision Trees. I already know how to implement these.

However, my teacher wants me to split the data in my .csv file into 80% and let my algorithms predict the other 20%. I would like to know how to actually split the data in that way.

diabetes_df = pd.read_csv("diabetes.csv")
diabetes_df.head()

with open("diabetes.csv", "rb") as f:
    data = f.read().split()
    train_data = data[:80]
    test_data = data[20:]

I tried to split it like this (sure it isn't working).

回答1:

Workflow

1. Load the data (see How do I read and write CSV files with Python? )
2. Preprocess the data (e.g. filtering / creating new features)
3. Make the train-test (validation and dev-set) split

Code

Sklearns sklearn.model_selection.train_test_split is what you are looking for:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=0)

回答2:

splitted_csv = "value1,value2,value3".split(',')
print(str(splitted_csv)) #["value1", "value2", "value3"]
print(splitted_csv[0]) #value1
print(splitted_csv[1]) #value2
print(splitted_csv[2]) #value3

There are also libraries that parse csv and allow you to access at value by column name, but from your example i thought that you need some "low level" way to do it

来源：https://stackoverflow.com/questions/50040238/train-test-split-of-a-csv-file-in-python