Here’s one way:

df['name'].value_counts()[df['name'].value_counts() == df['name'].value_counts().max()]

which prints:

helen    2
alex     2
Name: name, dtype: int64

You could use .apply and pd.value_counts to get a count the occurrence of all the names in the name column.

dataframe['name'].apply(pd.value_counts)

Not Obvious, But Fast

f, u = pd.factorize(df.name.values)
counts = np.bincount(f)
u[counts == counts.max()]

array(['alex', 'helen'], dtype=object)

You could try argmax like this:

dataframe['name'].value_counts().argmax()
Out[13]: 'alex'


The value_counts will return a count object of pandas.core.series.Series and argmax could be used to achieve the key of max values.

You can use this to get a perfect count, it calculates the mode a particular column

df['name'].value_counts()

To get the n most frequent values, just subset .value_counts() and grab the index:

# get top 10 most frequent names
n = 10
dataframe['name'].value_counts()[:n].index.tolist()

to get top 5:

dataframe['name'].value_counts()[0:5]

To get the top five most common names:

dataframe['name'].value_counts().head()

df['name'].value_counts()[:5].sort_values(ascending=False)

The value_counts will return a count object of pandas.core.series.Series and sort_values(ascending=False) will get you the highest values first.

my best solution to get the first is

 df['my_column'].value_counts().sort_values(ascending=False).argmax()

Simply use this..

dataframe['name'].value_counts().nlargest(n)

The functions for frequencies largest and smallest are:

• nlargest() for mostfrequent ‘n’ values
• nsmallest() for least frequent ‘n’ values

Use:

df['name'].mode()

or

df['name'].value_counts().idxmax()

n is used to get the number of top frequent used items

n = 2

a=dataframe['name'].value_counts()[:n].index.tolist()

dataframe["name"].value_counts()[a]

I had a similar issue best most compact answer to get lets say the top n (5 is default) most frequent values is:

df["column_name"].value_counts().head(n)

Identifying the top 5, for example, using value_counts

top5 = df['column'].value_counts()

Listing contents of ‘top_5’

top5[:5]

Getting top 5 most common lastname pandas:

df['name'].apply(lambda name: name.split()[-1]).value_counts()[:5]

It will give top five most common names:

df['name'].value_counts().nlargest(5)