如何根据空白列删除 CSV 文件中的行

Question

I have a csv file that is in this format, but has thousands of rows so I can summarize it like this

id,name,score1,score2,score3
1,,3.0,4.5,2.0
2,,,,
3,,4.5,3.2,4.1

I have tried to use.dropna() but that is not working.

My desired output is

id,name,score1,score2,score3
1,,3.0,4.5,2.0
3,,4.5,3.2,4.1

All I would really need is to check if score1 is empty because if score1 is empty then the rest of the scores are empty as well.

I have also tried this but it doesn't seem to do anything.

import pandas as pd

df = pd.read_csv('dataset.csv')

df.drop(df.index[(df["score1] == '')], axis=0,inplace=True)

df.to_csv('new.csv')

Can anyone help with this?

Answer 1

import pandas as pd


df = pd.DataFrame([[1,3.0,4.5,2.0],[2],[3,4.5,3.2,4.1]], columns=["id","score1","score2","score3"])

aux1 = df.dropna()
aux2 = df.dropna(axis='columns')
aux3 = df.dropna(axis='rows')

print('=== original ===')
print(df)
print()
print('=== mode 1 ===')
print(aux1)
print()
print('=== mode 2 ===')
print(aux2)
print()
print('=== mode 3 ===')
print(aux3)
print()
print('=== mode 4 ===')
print('drop original')
df.dropna(axis=1,inplace=True)
print(df)

Answer 2

After seeing your edits, I realized that dropna doesn't work for you because you have a None value in all of your rows. To filter for nan values in a specific column, I would recommend using the apply function like in the following code. (Btw the StackOverflow.csv is just a file where I copied and pasted your data from the question)

import pandas as pd
import math

df = pd.read_csv("StackOverflow.csv", index_col="id")

#Function that takes a number and returns if its nan or not
def not_nan(number):
    return not math.isnan(number)

#Filtering the dataframe with the function
df = df[df["score1"].apply(not_nan)]

What this does is iterate through the score1 row and check if a value is NaN or not. If it is, then it returns False. We then use the list of True and False values to filter out the values from the dataframe.