Python 计算某个值在数据帧列中出现的频率
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count the frequency that a value occurs in a dataframe column
提问by yoshiserry
I have a dataset
我有一个数据集
|category|
cat a
cat b
cat a
I'd like to be able to return something like (showing unique values and frequency)
我希望能够返回类似（显示唯一值和频率）
category | freq |
cat a 2
cat b 1
采纳答案by EdChum
Use groupbyand count:
使用groupby和count：
In [37]:
df = pd.DataFrame({'a':list('abssbab')})
df.groupby('a').count()
Out[37]:
a
a
a 2
b 3
s 2
[3 rows x 1 columns]
See the online docs: http://pandas.pydata.org/pandas-docs/stable/groupby.html
请参阅在线文档：http: //pandas.pydata.org/pandas-docs/stable/groupby.html
Also value_counts()as @DSM has commented, many ways to skin a cat here
同样value_counts()正如@DSM 所评论的，这里有很多剥猫皮的方法
In [38]:
df['a'].value_counts()
Out[38]:
b 3
a 2
s 2
dtype: int64
If you wanted to add frequency back to the original dataframe use transformto return an aligned index:
如果您想将频率添加回原始数据帧，请使用transform返回对齐的索引：
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]
回答by Shankar ARUL - jupyterdata.com
Using list comprehension and value_counts for multiple columns in a df
对 df 中的多列使用列表理解和 value_counts
[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)]
回答by Arran Cudbard-Bell
If you want to apply to all columns you can use:
如果要应用于所有列，可以使用：
df.apply(pd.value_counts)
This will apply a column based aggregation function (in this case value_counts) to each of the columns.
这会将基于列的聚合函数（在本例中为 value_counts）应用于每一列。
回答by Vidhya G
In 0.18.1 groupbytogether with countdoes not give the frequency of unique values:
在 0.18.1groupby和 withcount没有给出唯一值的频率：
>>> 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:
但是，可以使用size以下方法轻松确定唯一值及其频率：
>>> 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.
随着df.a.value_counts()排序的值（按降序排列，即最大价值第一）默认情况下返回。
回答by Timz95
Without any libraries, you could do this instead:
没有任何库，你可以这样做：
def to_frequency_table(data):
frequencytable = {}
for key in data:
if key in frequencytable:
frequencytable[key] += 1
else:
frequencytable[key] = 1
return frequencytable
Example:
例子：
to_frequency_table([1,1,1,1,2,3,4,4])
>>> {1: 4, 2: 1, 3: 1, 4: 2}
回答by Roman Kazakov
df.apply(pd.value_counts).fillna(0)
value_counts- Returns object containing counts of unique values
value_counts- 返回包含唯一值计数的对象
apply- count frequency in every column. If you set axis=1, you get frequency in every row
apply- 计算每列中的频率。如果你设置axis=1，你会得到每一行的频率
fillna(0) - make output more fancy. Changed NaN to 0
fillna(0) - 使输出更花哨。将 NaN 更改为 0
回答by user666
If your DataFrame has values with the same type, you can also set return_counts=Truein numpy.unique().
如果您的 DataFrame 具有相同类型的值，您还可以return_counts=True在numpy.unique() 中进行设置。
index, counts = np.unique(df.values,return_counts=True)
index, counts = np.unique(df.values,return_counts=True)
np.bincount()could be faster if your values are integers.
如果您的值是整数，np.bincount()可能会更快。
回答by Satyajit Dhawale
df.category.value_counts()
This short little line of code will give you the output you want.
这短短的一小行代码将为您提供所需的输出。
If your column name has spaces you can use
如果您的列名有空格，您可以使用
df['category'].value_counts()
回答by tsando
You can also do this with pandas by broadcasting your columns as categories first, e.g. dtype="category"e.g.
您也可以通过首先将您的列作为类别广播来对 Pandas 执行此操作，dtype="category"例如
cats = ['client', 'hotel', 'currency', 'ota', 'user_country']
df[cats] = df[cats].astype('category')
and then calling describe:
然后调用describe：
df[cats].describe()
This will give you a nice table of value counts and a bit more :):
这将为您提供一个很好的值计数表和更多:)：
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
回答by RAHUL KUMAR
n_values = data.income.value_counts()
First unique value count
第一个唯一值计数
n_at_most_50k = n_values[0]
Second unique value count
第二个唯一值计数
n_greater_50k = n_values[1]
n_values
Output:
输出：
<=50K 34014
>50K 11208
Name: income, dtype: int64
Output:
输出：
n_greater_50k,n_at_most_50k:-
(11208, 34014)
