Question :
Get total values_count from a dataframe with Python Pandas
I have a Python pandas dataframe with several columns. Now I want to copy all values into one single column to get a values_count result alle values included. At the end I need the total count of string1, string2, n. What is the best way to do it?
index row 1 row 2 ...
0 string1 string3
1 string1 string1
2 string2 string2
...
Answer #1:
If performance is an issue try:
from collections import Counter
Counter(df.values.ravel())
#Counter({'string1': 3, 'string2': 2, 'string3': 1})
Or stack it into one Series then use value_counts
df.stack().value_counts()
#string1 3
#string2 2
#string3 1
#dtype: int64
For larger (long) DataFrames with a small number of columns, looping may be faster than stacking:
s = pd.Series()
for col in df.columns:
s = s.add(df[col].value_counts(), fill_value=0)
#string1 3.0
#string2 2.0
#string3 1.0
#dtype: float64
Also, there’s a numpy solution:
import numpy as np
np.unique(df.to_numpy(), return_counts=True)
#(array(['string1', 'string2', 'string3'], dtype=object),
# array([3, 2, 1], dtype=int64))
df = pd.DataFrame({'row1': ['string1', 'string1', 'string2'],
'row2': ['string3', 'string1', 'string2']})
def vc_from_loop(df):
s = pd.Series()
for col in df.columns:
s = s.add(df[col].value_counts(), fill_value=0)
return s
Small DataFrame
%timeit Counter(df.values.ravel())
#11.1 µs ± 56.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit df.stack().value_counts()
#835 µs ± 5.46 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit vc_from_loop(df)
#2.15 ms ± 34.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit np.unique(df.to_numpy(), return_counts=True)
#23.8 µs ± 241 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Long DataFrame
df = pd.concat([df]*300000, ignore_index=True)
%timeit Counter(df.values.ravel())
#124 ms ± 1.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit df.stack().value_counts()
#337 ms ± 3.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit vc_from_loop(df)
#182 ms ± 1.58 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit np.unique(df.to_numpy(), return_counts=True)
#1.16 s ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
