The below code is being used to analysis a csv file and at the moment im trying to remove the columns of the array which are not in my check_list. This only checks the first row and if the first row of the particular column doesnt belong to the check_list it removes the entire column. But this error keeps getting thrown and not sure how to avoid it.

import numpy as np
def load_metrics(filename):
    """opens a csv file and returns stuff"""
    check_list = ["created_at","tweet_ID","valence_intensity","anger_intensity","fear_intensity","sadness_intensity","joy_intensity","sentiment_category","emotion_category"]
    file=open(filename)
    data = []
    for lin in file:
        lin = lin.strip()
        lin = lin.split(",")
        data.append(lin)
    for col in range(len(data[0])):
        if np.any(data[0][col] not in check_list) == True:
            data[0]= np.delete(np.array(data), col, 1)
        print(col)
    return np.array(data)

The below test is being used on the code too:

data = load_metrics("covid_sentiment_metrics.csv")
print(data[0])

Test results:

['created_at' 'tweet_ID' 'valence_intensity' 'anger_intensity'
 'fear_intensity' 'sadness_intensity' 'joy_intensity' 'sentiment_category'
 'emotion_category']

CodePudding user response：

Change your load_metrics function to:

def load_metrics(filename):
    check_list = ["created_at","tweet_ID","valence_intensity","anger_intensity",
        "fear_intensity","sadness_intensity","joy_intensity","sentiment_category",
        "emotion_category"]
    data = []
    with open(filename, 'r') as file:
        for lin in file:
            lin = lin.strip()
            lin = lin.split(",")
            data.append(lin)
    arr = np.array(data)
    colFilter = []
    for col in arr[0]:
        colFilter.append(col in check_list)
    return arr[:, colFilter]

I introduced the following corrections:

1. Use with to automatically close the input file (your code fails to close it).
2. Create a "full" Numpy array (all columns) after the data has been read.
3. Compute colFilter list - which columns are in check_list.
4. Return only filtered columns.

CodePudding user response：

Read columns by checklist

This code does not include checks related to reading a file or a broken data structure, so that the main idea is more or less clear. So, here I assume that a csv-file exists and has at least 2 lines:

import numpy as np

def load_metrics(filename, check_list):
    """open a csv file and return data as numpy.array
       with columns from a check list"""
    data = []
    with open(filename) as file:
        headers = file.readline().rstrip("\n").split(",")
        for line in file:
            data.append(line.rstrip("\n").split(","))
    col_to_remove = []
    for col in reversed(range(len(headers))):
        if headers[col] not in check_list:
            col_to_remove.append(col)
            headers.pop(col)
    data = np.delete(np.array(data), col_to_remove, 1)
    return data, headers

Quick testing:

test_data = """\
hello,some,other,world
1,2,3,4
5,6,7,8
"""

with open("test.csv",'w') as f:
    f.write(test_data) 

check_list = ["hello","world"]
d, h = load_metrics("test.csv", ["hello","world"])
print(d, h)

Expected output:

[['1' '4']
 ['5' '8']] ['hello', 'world']

Some details:

• Instead of np.any(data[0][col] not in check_list) == True would be enough data[0][col] not in check_list
• Stripping with default parameters is not good as far as you can delete meaningful spaces.
• Do not delete anything while looping forward. But we can do it (with some reservations) while looping backward.
• check_list is better as a parameter.
• Separate data and headers as they may have different types.
• In your case it is better to use pandas.read_csv, see the picture below.