In Python
, one can get the counts of values in a list by using Series.value_counts()
:
import pandas as pd
df = pd.DataFrame()
df['x'] = ['a','b','b','c','c','d']
df['y'] = list(range(1,7))
df['x'].value_counts()
c 2
b 2
a 1
d 1
Name: x, dtype: int64
In R
, I have to use three separate commands.
df <- tibble(x=c('a','b','b','c','c','d'), y=1:6)
df %>% group_by(x) %>% summarise(n=n()) %>% arrange(desc(n))
x n
b 2
c 2
a 1
d 1
Is there a shorter / more idiomatic way of doing this in R? Or am I better off writing a custom function?
Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element.
By default, value_counts will sort the data by numeric count in descending order. The ascending parameter enables you to change this. When you set ascending = True , value counts will sort the data by count from low to high (i.e., ascending order).
By default, the value_counts() function does not show the frequency of NaN values.
count() should be used when you want to find the frequency of valid values present in columns with respect to specified col . . value_counts() should be used to find the frequencies of a series.
Pandas Series.value_counts () function return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element.
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type.
Series containing counts of unique values in Pandas . The value_counts() function is used to get a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Syntax:
Value_counts () with sort_index (ascending=True) sorts by index (column that you are running value_counts () on: If you want to list value_counts () in reverse alphabetical order you will need to change ascending to False sort_index (ascending=False) 4.) Pandas value_counts (): sort by value, then alphabetically
The tidyverse has dplyr::count
, which is a shortcut for 'group_by' and 'summarize' to get counts.
df <- tibble(x=c('a','b','b','c','c','d'), y=1:6)
dplyr::count(df, x, sort = TRUE)
# A tibble: 4 x 2
x n
<chr> <int>
1 b 2
2 c 2
3 a 1
4 d 1
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