How to check multiple conditions on dataframe columns, row by row, without using a loop Pandas

Question

I have a big dataframe df that I need to clean from bad data using four different conditions:

1. if the element of column A is a number
2. if this number is different from nan
3. if the element of column B belongs to list1
4. if the element of column C belongs to list2

I would like to check these conditions without a loop because it runs very slowly. This is the code I wrote:

df1=pd.read_csv(data1)
list1=pd.read_csv(data2)
list2=pd.read_csv(data3)
col_df= #list of target column names (it's not important to specify what they are)
df2=pd.DataFrame(np.zeros(shape=(1,len(col_df))),columns=col_df)
row_df3=pd.DataFrame(np.zeros(shape=(1,len(col_df))),columns=col_df) #I use it to append new zero row to previous
k1=0 #index to add new row to df2
for j in range(len(df1)):
        try:
            float(df1['A'][j])
            check=1
        except:
            check=0
        if check==1: #if the element is a number
            if df1['B'][j] in list1['B'].unique(): #if the element is in the first list
                if str(df1['A'][j])!='nan': #if the element value is different from nan
                    if da['C'][j] in list2['C'].unique(): #if the element is in the second list
                        iii=list1.index[list1['B']==df1['B'][j]] #I take the index in list1
                        iii=iii[0]
                        df2.loc[k1]=row_df2.loc[0] #new zero row
                        df2.loc[k1]=df1.iloc[j] #assign elements to new df2 with same column name of df1
                        df2['B'][k1]=list1['B'][iii] #assign element from list1
                        k1=k1+1 #new row for df2

It works but slowly. Is there a way to write all this conditions without using a loop? If yes I also thought to use drop function instead of creating a new dataframe df2.

Thank you in advance!

I tried to do this:

def is_float(element: any) -> bool:
    #If you expect None to be passed:
    if element is None: 
        return False
    try:
        float(element)
        return True
    except ValueError:
        return False
df1['Bool']=df1['A'].apply(is_float) #to create a new column that check if it's a number
df2=df1[df1['Bool']==True and df1['B'] in list1['B'].unique() and da['C'] in list2['C'].unique()] #to check all other conditions

without defining df2 previous this time, but it doesn't work.. I get two errors:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

and if I erase the first condition, so

df2=df1[df1['B'] in list1['B'].unique() and da['C'] in list2['C'].unique()]

I get:

ValueError: ('Lengths must match to compare')

Answer 1

using apply and those functions you should be able to make a column grouping all conditions together

def is_null(value):
    # Null values are considered as float but nan is not equal to nan
    if pd.isna(value):
        return True 
    else: 
        return False

def is_float(value):
    if type(value) == float:
        return True 
    else: 
        return False

def in_list1(value):
    if value in list1: 
        return True 
    else: 
        return False 

def in_list2(value):
    if value in list2: 
        return True 
    else: 
        return False