Frequency table for a single variable
The answer provided by @DSM is simple and straightforward, but I thought I'd add my own input to this question. If you look at the code for pandas.value_counts, you'll see that there is a lot going on.
If you need to calculate the frequency of many series, this could take a while. A faster implementation would be to use numpy.unique with return_counts = True
Here is an example:
import pandas as pd
import numpy as np
my_series = pd.Series([1,2,2,3,3,3])
print(my_series.value_counts())
3 3
2 2
1 1
dtype: int64
Notice here that the item returned is a pandas.Series
In comparison, numpy.unique returns a tuple with two items, the unique values and the counts.
vals, counts = np.unique(my_series, return_counts=True)
print(vals, counts)
[1 2 3] [1 2 3]
You can then combine these into a dictionary:
results = dict(zip(vals, counts))
print(results)
{1: 1, 2: 2, 3: 3}
And then into a pandas.Series
print(pd.Series(results))
1 1
2 2
3 3
dtype: int64
for frequency distribution of a variable with excessive values you can collapse down the values in classes,
Here I excessive values for employrate variable, and there's no meaning of it's frequency distribution with direct values_count(normalize=True)
country employrate alcconsumption
0 Afghanistan 55.700001 .03
1 Albania 11.000000 7.29
2 Algeria 11.000000 .69
3 Andorra nan 10.17
4 Angola 75.699997 5.57
.. ... ... ...
208 Vietnam 71.000000 3.91
209 West Bank and Gaza 32.000000
210 Yemen, Rep. 39.000000 .2
211 Zambia 61.000000 3.56
212 Zimbabwe 66.800003 4.96
[213 rows x 3 columns]
frequency distribution with values_count(normalize=True) with no classification,length of result here is 139 (seems meaningless as a frequency distribution):
print(gm["employrate"].value_counts(sort=False,normalize=True))
50.500000 0.005618
61.500000 0.016854
46.000000 0.011236
64.500000 0.005618
63.500000 0.005618
58.599998 0.005618
63.799999 0.011236
63.200001 0.005618
65.599998 0.005618
68.300003 0.005618
Name: employrate, Length: 139, dtype: float64
putting classification we put all values with a certain range ie.
0-10 as 1, 11-20 as 2 21-30 as 3, and so forth.
gm["employrate"]=gm["employrate"].str.strip().dropna()
gm["employrate"]=pd.to_numeric(gm["employrate"])
gm['employrate'] = np.where(
(gm['employrate'] <=10) & (gm['employrate'] > 0) , 1, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=20) & (gm['employrate'] > 10) , 1, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=30) & (gm['employrate'] > 20) , 2, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=40) & (gm['employrate'] > 30) , 3, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=50) & (gm['employrate'] > 40) , 4, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=60) & (gm['employrate'] > 50) , 5, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=70) & (gm['employrate'] > 60) , 6, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=80) & (gm['employrate'] > 70) , 7, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=90) & (gm['employrate'] > 80) , 8, gm['employrate']
)
gm['employrate'] = np.where(
(gm['employrate'] <=100) & (gm['employrate'] > 90) , 9, gm['employrate']
)
print(gm["employrate"].value_counts(sort=False,normalize=True))
after classification we have a clear frequency distribution.
here we can easily see, that 37.64% of countries have employ rate between 51-60%
and 11.79% of countries have employ rate between 71-80%
5.000000 0.376404
7.000000 0.117978
4.000000 0.179775
6.000000 0.264045
8.000000 0.033708
3.000000 0.028090
Name: employrate, dtype: float64
You can use list comprehension on a dataframe to count frequencies of the columns as such
[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)]
Breakdown:
my_series.select_dtypes(include=['O'])
Selects just the categorical data
list(my_series.select_dtypes(include=['O']).columns)
Turns the columns from above into a list
[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)]
Iterates through the list above and applies value_counts() to each of the columns
Maybe .value_counts()?
>>> import pandas
>>> my_series = pandas.Series([1,2,2,3,3,3, "fred", 1.8, 1.8])
>>> my_series
0 1
1 2
2 2
3 3
4 3
5 3
6 fred
7 1.8
8 1.8
>>> counts = my_series.value_counts()
>>> counts
3 3
2 2
1.8 2
fred 1
1 1
>>> len(counts)
5
>>> sum(counts)
9
>>> counts["fred"]
1
>>> dict(counts)
{1.8: 2, 2: 2, 3: 3, 1: 1, 'fred': 1}