Get Most Frequent String from a Data Frame Column

Pandas get the most frequent values of a column

By using mode

df.name.mode()
Out[712]: 
0     alex
1    helen
dtype: object

Get most frequent string from a data frame column

It seems like you need something like:

Function

freqfunc <- function(x, n){
  tail(sort(table(unlist(strsplit(as.character(x), ", ")))), n)
}

Testing on your data set

freqfunc(gaps$MissingDates, 5) # Five most frequent dates

## 1996-12-26 1997-12-26 1998-01-02 1999-12-31 2001-09-12 
##          4          4          4          4          4 

Most frequent element from a data.frame

I would suggest a tidyverse approach:

library(tidyr)
library(dplyr)
#Separate rows
df %>% separate_rows(`Most frequent`,2,sep = ',') %>%
  group_by(`Most frequent`) %>%
  summarise(N=n()) %>% arrange(desc(N))

Output:

   Most frequent  N
1           1110 24
2            110 22
3            310 19
4           8210 16
5            210 12
6           3210  6
7           9120  6
8           3830  5
9           5190  4
10           111  3
11          3110  3
12          3819  2
13          1120  1
14      27080618  1
15          9210  1

Which is similar to @AllanCameron results.

How to get the n most frequent or top values per column in python pandas?

This should work

n = 2
df.apply(lambda x: pd.Series(x.value_counts().index[:n]))

Dataframe - get most frequent values and their count

I believe you need DataFrame with 2 columns filled by top10 values:

 df1 = df['product'].value_counts().iloc[:10].rename_axis('val').reset_index(name='count')

How to get the most frequent row in table

In Pandas 1.1.0. is possible to use the method value_counts() to count unique rows in DataFrame:

df.value_counts()

Output:

col_1  col_2  col_3
1      1      A        2
       0      C        1
              B        1
              A        1
0      1      A        1

This method can be used to find the most frequent row:

df.value_counts().head(1).index.to_frame(index=False)

Output:

   col_1  col_2 col_3
0      1      1     A

How to get the number of the most frequent value in a column?

It looks like you may have some nulls in the column. You can drop them with df = df.dropna(subset=['item']). Then df['item'].value_counts().max() should give you the max counts, and df['item'].value_counts().idxmax() should give you the most frequent value.

Get the most frequent entry

library(dplyr)

df %>%
  group_by(Name) %>% 
  count(City) %>%
  top_n(1)

# Selecting by n
# # A tibble: 2 x 3
# # Groups:   Name [2]
#   Name  City         n
#   <chr> <chr>    <int>
# 1 A     New York     3
# 2 B     Chicago      2 

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