Accessing first column of pandas value_counts

Question

I'm trying to use value_counts() function from Python's pandas package to find the frequency of items in a column. This works and outputs the following:

57     1811
62      630
71      613
53      217
59      185
68       88
52       70

Name: hospitalized, dtype: int64

In which the first column is the item and the right column is its frequency in the column.

From there, I wanted to access the first column of items and iterate through that in a for loop. I want to be able to access the item of each row and check if it is equal to another value. If this is true, I want to be able to access the second column and divide it by another number.

My big issue is accessing the first column from the .value_counts() output. Is it possible to access this column and if so, how? The columns aren't named anything specific (since it's just the value_counts() output) so I'm unsure how to access them.

Renaud · Accepted Answer · 2023-08-31 15:28:25Z

38

Use Panda's items():

df = pd.DataFrame({'mycolumn': [1,2,2,2,3,3,4]})
for val, cnt in df.mycolumn.value_counts().items():
    print('value', val, 'was found', cnt, 'times')

value 2 was found 3 times
value 3 was found 2 times
value 4 was found 1 times
value 1 was found 1 times

edited Aug 31, 2023 at 15:28

answered Mar 21, 2017 at 10:52

Renaud

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2 Comments

Akash Sonthalia Over a year ago

Clean approach!

N4ppeL Over a year ago

getting AttributeError: 'Series' object has no attribute 'iteritems'. .items() will work though.

user2285236 · Accepted Answer · 2016-05-02 00:13:10Z

21

value_counts returns a Pandas Series:

df = pd.DataFrame(np.random.choice(list("abc"), size=10), columns = ["X"])
df["X"].value_counts()
Out[243]: 
c    4
b    3
a    3
Name: X, dtype: int64

For the array of individual values, you can use the index of the Series:

vl_list = df["X"].value_counts().index
Index(['c', 'b', 'a'], dtype='object')

It is of type "Index" but you can iterate over it:

for idx in vl_list:
    print(idx)

c
b
a

Or for the numpy array, you can use df["X"].value_counts().index.values

answered May 2, 2016 at 0:13

user2285236

2 Comments

PaulG Over a year ago

Is .index or what? How did you know you could call .index on value_counts()? I checked the pandas docs and don't see any available attributes for value_counts(), and yet here I see .index and .name both working (i.e. value_counts().name). Where do I find that information?

Maddenker Over a year ago

It returns a Series. You can see the .index syntax here:pandas.pydata.org/pandas-docs/stable/dsintro.html#series

MCheng · Accepted Answer · 2020-05-19 04:08:28Z

2

You can access the first column by using .keys() or index as below:

df.column_name.value_counts().keys()

df.column_name.value_counts().index

answered May 19, 2020 at 4:08

MCheng

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Comments

smcs · Accepted Answer · 2024-07-24 14:46:11Z

0

I tried to find and then iterate all value pairs across two different columns and eventually resorted to this:

pairs = df.value_counts(['A', 'B']).reset_index()
for i in range(0, len(pairs)):
    print(pairs.loc[i]['A'])
    print(pairs.loc[i]['B'])
    print(pairs.loc[i]['count'])

answered Jul 24, 2024 at 14:46

smcs

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Comments

Eamonn Kenny · Accepted Answer · 2025-10-23 11:40:06Z

0

I found obtaining the first value a little non-intuitive:

     counter = df[columnName].value_counts()
     print( "Item:", counter.index[0], "with value:", counter.iloc[0] )

answered Oct 23 at 11:40

Eamonn Kenny

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