PYTHON

drop if nan in column pandas

df = df[df['EPS'].notna()]

check if a value in dataframe is nan

#return a subset of the dataframe where the column name value == NaN 
df.loc[df['column name'].isnull() == True] 

check if a value is nan pandas

import numpy as np
import pandas as pd

val = np.nan

print(pd.isnull(val))
# True

python pandas how to check in what columns there are empty values(NaN)

import pandas as pd
df = pd.read_csv("file path.csv")
null=pd.DataFrame(df.isnull().sum(),columns=["Null Values count"]) # gets a dataset with all of the columns and the number of Null values in it
null["Null values percentage"]=(df.isna().sum()/len(df)*100) # adds a column with the % of the Null out of all of the column
null = null[null["Null values percentage"] > 0] # keep only the ones that has Null values
null.style.background_gradient() # prints it in a pretty way