Count each value in a column within the column in dataframe

Question

I am struggling to do the below and was wondering if you can help me.

I have a pandas data frame of 2 columns. I want to count each value in column A within Column A. If the occurrence is more than 1, print('hello')

For instance,

for i in range(1, len(DF)) **if count(DF.iloc[[i],[1]].values)>1:** print('hello') any help please?

Thanks, H

Answer 1

If you want to print 'hello' if there is more than one occurence:

if (df['A'] > 1).count() > 1:
    print('hello')

If you want to print 'hello' at each occurence:

You could use df.apply() to apply a printing lambda function to your column:

df['A'].apply(lambda x: print('hello') if x > 1 else None)

Or count all values in A which are greater than one and print in a loop:

for i in range(0, (df['A'] > 1).count()):
    print('hello')

Answer 2

I think you need Series.value_counts

if you want to print once hello if there is any value that is repeated use:

if df['A'].value_counts().gt(1).any():
    print('hello')

if you want to print hello for each value in the series that is repeated:

print('Hello\n'*df['A'].map(df['A'].value_counts().gt(1)).sum())

---

if you want to print hello for each unique value in the series that is repeated

print('Hello\n'*df['A'].value_counts().gt(1).sum())

---

Example

df = pd.DataFrame({'A':[1,2,3,1,4,4,5,7,8,9,3]})
print(df)

    A
0   1
1   2
2   3
3   1
4   4
5   4
6   5
7   7
8   8
9   9
10  3

if df['A'].value_counts().gt(1).any():
    print('hello\n')
print('2 Method\n')    
print('Hello\n'*df['A'].map(df['A'].value_counts().gt(1)).sum())
print('3 Method\n')
print('Hello\n'*df['A'].value_counts().gt(1).sum())

Output

hello

2 Method

Hello
Hello
Hello
Hello
Hello
Hello

3 Method

Hello
Hello
Hello