In 0.18.1 groupby
together with count
does not give the frequency of unique values:
>>> df
a
0 a
1 b
2 s
3 s
4 b
5 a
6 b
>>> df.groupby('a').count()
Empty DataFrame
Columns: []
Index: [a, b, s]
However, the unique values and their frequencies are easily determined using size
:
>>> df.groupby('a').size()
a
a 2
b 3
s 2
With df.a.value_counts()
sorted values (in descending order, i.e. largest value first) are returned by default.
I believe this should work fine for any DataFrame columns list.
def column_list(x):
column_list_df = []
for col_name in x.columns:
y = col_name, len(x[col_name].unique())
column_list_df.append(y)
return pd.DataFrame(column_list_df)
column_list_df.rename(columns={0: "Feature", 1: "Value_count"})
The function "column_list" checks the columns names and then checks the uniqueness of each column values.
Using list comprehension and value_counts for multiple columns in a df
[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)]
df.apply(pd.value_counts).fillna(0)
value_counts - Returns object containing counts of unique values
apply - count frequency in every column. If you set axis=1
, you get frequency in every row
fillna(0) - make output more fancy. Changed NaN to 0
your data:
|category|
cat a
cat b
cat a
solution:
df['freq'] = df.groupby('category')['category'].transform('count')
df = df.drop_duplicates()
df.category.value_counts()
This short little line of code will give you the output you want.
If your column name has spaces you can use
df['category'].value_counts()
You can also do this with pandas by broadcasting your columns as categories first, e.g. dtype="category"
e.g.
cats = ['client', 'hotel', 'currency', 'ota', 'user_country']
df[cats] = df[cats].astype('category')
and then calling describe
:
df[cats].describe()
This will give you a nice table of value counts and a bit more :):
client hotel currency ota user_country
count 852845 852845 852845 852845 852845
unique 2554 17477 132 14 219
top 2198 13202 USD Hades US
freq 102562 8847 516500 242734 340992
If you want to apply to all columns you can use:
df.apply(pd.value_counts)
This will apply a column based aggregation function (in this case value_counts) to each of the columns.
If your DataFrame has values with the same type, you can also set return_counts=True
in numpy.unique().
index, counts = np.unique(df.values,return_counts=True)
np.bincount() could be faster if your values are integers.
Without any libraries, you could do this instead:
def to_frequency_table(data):
frequencytable = {}
for key in data:
if key in frequencytable:
frequencytable[key] += 1
else:
frequencytable[key] = 1
return frequencytable
Example:
to_frequency_table([1,1,1,1,2,3,4,4])
>>> {1: 4, 2: 1, 3: 1, 4: 2}
@metatoaster has already pointed this out.
Go for Counter
. It's blazing fast.
import pandas as pd
from collections import Counter
import timeit
import numpy as np
df = pd.DataFrame(np.random.randint(1, 10000, (100, 2)), columns=["NumA", "NumB"])
%timeit -n 10000 df['NumA'].value_counts()
# 10000 loops, best of 3: 715 µs per loop
%timeit -n 10000 df['NumA'].value_counts().to_dict()
# 10000 loops, best of 3: 796 µs per loop
%timeit -n 10000 Counter(df['NumA'])
# 10000 loops, best of 3: 74 µs per loop
%timeit -n 10000 df.groupby(['NumA']).count()
# 10000 loops, best of 3: 1.29 ms per loop
Cheers!
n_values = data.income.value_counts()
First unique value count
n_at_most_50k = n_values[0]
Second unique value count
n_greater_50k = n_values[1]
n_values
Output:
<=50K 34014
>50K 11208
Name: income, dtype: int64
Output:
n_greater_50k,n_at_most_50k:-
(11208, 34014)
Source: Stackoverflow.com