Counting the Frequency of an Element in a Data Frame

Count frequency of values in pandas DataFrame column

You can use value_counts and to_dict:

print df['status'].value_counts()
N    14
S     4
C     2
Name: status, dtype: int64

counts = df['status'].value_counts().to_dict()
print counts
{'S': 4, 'C': 2, 'N': 14}

How to count the frequency of the elements in a dataframe's row?

Given your df:

>>> df

  col1 col2 col3  col4  col5
0  pqr  abc  pqr   NaN   NaN

You can use stack() and value_counts():

df.stack().value_counts().reset_index().\
    rename({'index':'code',
            0:'count'},axis=1)

Prints:

  code  count
0  pqr      2
1  abc      1

Count the frequency that a value occurs in a dataframe column

Use value_counts() as @DSM commented.

In [37]:
df = pd.DataFrame({'a':list('abssbab')})
df['a'].value_counts()

Out[37]:

b    3
a    2
s    2
dtype: int64

Also groupby and count. Many ways to skin a cat here.

In [38]:
df.groupby('a').count()

Out[38]:

   a
a   
a  2
b  3
s  2

[3 rows x 1 columns]

See the online docs.

If you wanted to add frequency back to the original dataframe use transform to return an aligned index:

In [41]:
df['freq'] = df.groupby('a')['a'].transform('count')
df

Out[41]:

   a freq
0  a    2
1  b    3
2  s    2
3  s    2
4  b    3
5  a    2
6  b    3

[7 rows x 2 columns]

How to count frequency of values across the entire dataframe

You can use the stack function to stack all values in one column, and then use value_counts:

df.stack().value_counts()

Splitting and counting the frequency of the elements of a Pandas column

Try with the something as follows

from collections import Counter  

def most_common_words(labels, quantity):
    """
    Split all words present in list and count how many times it
    is repeated in the list. 
    Args:
        labels (list): List of strings to split.
        quantity (int): Amount of most common words to return.
    Returns:
        counter (liste): List of words splitted with its number of ocurrences.
    """
    #words = [i.split(" ", 3)[0] for i in labels]
    #counter = Counter(words).most_common(quantity)
    words = [(re.split('(;|,|-| |\*|\n)', i)) for i in labels]
    counter = Counter(x for xs in words for x in set(xs)).most_common(quantity)
    df = pd.DataFrame(counter, columns=["Word", "Occurence number"])\
                        .sort_values(by="Occurence number", ascending=True)
    
    df = df[df["Word"] != " "].reset_index(drop=True)
    
    return df
    
df_most_common_words = most_common_words(data_copy["col"].tolist(), 20)
print(df_most_common_words)

the output

            Word  Occurence number
19    Repetition              8946
18    Government              9159
17       SACMEQ:             11502
16         Gross             12993
15        PIAAC:             20874
14         PISA:             21087
13        TIMSS:             21300
12        Africa             21513
11     Enrolment             21939

In your case, you can do something as follows.

col_a = ['A', 'B', 'A-E', 'a', 'A;e', 'B;e', 'A;B', 'C A', 'As']
df = pd.DataFrame(col_a, columns=['col_a'])
df
    col_a
0   A
1   B
2   A-E
3   a
4   A;e
5   B;e
6   A;B
7   C A
8   As

df['col_a'] = df['col_a'].str.replace('-',' ').str.replace(';',' ')
df
    col_a
0   A
1   B
2   A E
3   a
4   A e
5   B e
6   A B
7   C A
8   As

df_most_common_words = most_common_words(df["col_a"].tolist(), 20)
df_most_common_words    
  Word  Occurence number
0   E   1
1   a   1
2   C   1
3   As  1
4   e   2
5   B   3
6   A   5

Counting the frequency of an element in a data frame

You can turn your data.frame to a vector and then use table

df <- read.table(text = "   b    c    e    f    g    h    j 
1 Dxa2 Dxa2 Dxa2 Dxa2 Dxa2 Dxa2 Dxa2
2 Dxb2 Dxb2 Dxb2 Dxb2 Dxc2 Dxc2 Dxc2
3 Dxd2 Dxi2 tneg tpos Dxd2 Dxi2 tneg", header = TRUE, row.names = 1)

table(unlist(df))
## Dxa2 Dxb2 Dxd2 Dxi2 tneg tpos Dxc2 
##    7    4    2    2    2    1    3 

You can turn the result to a data.frame too

as.data.frame(table(unlist(df)))
##   Var1 Freq
## 1 Dxa2    7
## 2 Dxb2    4
## 3 Dxd2    2
## 4 Dxi2    2
## 5 tneg    2
## 6 tpos    1
## 7 Dxc2    3

How to count frequency of elements from a columns of lists in pyspark dataframe?

The following command explodes the array, and provides the count of each element

import pyspark.sql.functions as F

df_ans = (df2
           .withColumn("explode", F.explode("My_list"))
           .groupBy("explode")
           .count()
           .orderBy(F.desc("count"))

the result is

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