Count Frequency of Values in Pandas Dataframe Column

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}

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]

Count frequency of values in pandas DataFrame

Try:

result = df.apply(pd.value_counts).fillna(0)

   col1  col2  col3  col4  col5  col6  col7  col8
A   5.0   1.0   0.0   0.0   5.0   5.0   0.0   0.0
C   0.0   0.0   1.0   4.0   2.0   0.0   6.0   1.0
G   1.0   1.0   6.0   3.0   0.0   1.0   0.0   0.0
T   1.0   5.0   0.0   0.0   0.0   1.0   1.0   6.0

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()

Pandas: how to count the frequency of words in a column based on another column

You can first select base on column2 then use .values_counts(). try this:

>>> df[df['column2'] == 'Ford']['column4'].value_counts()

As DataFrame:

>>> pd.DataFrame(df[df['column2'] == 'Ford']['column4'].value_counts()
                ).reset_index()\
.rename(columns={'index':'model', 'column4':'counts'}
)
    model       counts
0   Ford        2
1   Mustang     1

pandas count frequency of column value in another dataframe column

Use Series.map by Series created by Series.value_counts, last replace missing values to 0:

df2["freq"] = df2["col2"].map(df1["col1"].value_counts()).fillna(0).astype(int)
print (df2)
   col2  freq
0   636     2
1   734     0
2   801     1
3   803     0

Count frequency of categories in column across subsets of data

I believe you're looking for pd.crosstab:

>>> pd.crosstab(index=df["Growth Class 2"], columns=[df.Date, df.Plot])

Date         2021-06-14                2021-06-20          2021-06-28          2021-07-05
Plot                 C1 C2 C3 C4 T1 T2         C3 C4 T2 T4         C1 C4 T1 T4         T2 T4
GrowthClass2
I                     1  1  0  0  0  1          0  1  0  0          0  0  0  1          0  1
S                     1  1  0  0  1  0          1  0  0  0          0  0  0  0          0  0
SS                    0  0  0  1  0  0          0  0  0  0          0  1  0  0          0  0
V                     1  0  1  1  0  0          0  0  1  1          1  0  1  0          1  0

Count the frequency that a bunch of values occurs in a dataframe column

IIUC, use pd.cut:

out = df.groupby(pd.cut(df['col2'], np.linspace(0, 1, 101)))['col1'].sum()
print(out)

# Output
col2
(0.0, 0.01]     33
(0.01, 0.02]     0
(0.02, 0.03]    31
(0.03, 0.04]    12
(0.04, 0.05]     0
                ..
(0.95, 0.96]     0
(0.96, 0.97]     0
(0.97, 0.98]     0
(0.98, 0.99]     0
(0.99, 1.0]      0
Name: col1, Length: 100, dtype: int64

R Count Frequency of Custom Dictionary in a Dataframe Column but Group them

Try group_by() and summarise() and you can spread() after to create a column for each year.

See if this works for your:

freq_auth <- tweetsanalysis1 %>%
        mutate(authority_dic =str_c(str_extract(text, str_c(authority_dic, collapse = '|')))) %>%
        group_by(authority_dic, year, user_username) %>%
        summarise(freq_word = n()) %>% 
        arrange(desc(freq_word)) %>%
        spread(year, freq_word)

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