Python 熊猫计算特定值的出现次数

我试图找出某个值在一个列中出现的次数。

我用 data = pd.DataFrame.from_csv('data/DataSet2.csv')做了一个数据框架

现在我想知道某个东西在一列中出现的次数，这是怎么做到的？

我认为这是下面，我在教育栏目中看到，并计算时间 ?发生。

下面的代码显示我试图找出 9th出现的次数，错误是我运行代码时得到的

密码

missing2 = df.education.value_counts()['9th']
print(missing2)

错误

KeyError: '9th'
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最佳答案

You can create subset of data with your condition and then use shape or len:

print df
col1 education
0    a       9th
1    b       9th
2    c       8th


print df.education == '9th'
0     True
1     True
2    False
Name: education, dtype: bool


print df[df.education == '9th']
col1 education
0    a       9th
1    b       9th


print df[df.education == '9th'].shape[0]
2
print len(df[df['education'] == '9th'])
2

Performance is interesting, the fastest solution is compare numpy array and sum:

graph

Code:

import perfplot, string
np.random.seed(123)




def shape(df):
return df[df.education == 'a'].shape[0]


def len_df(df):
return len(df[df['education'] == 'a'])


def query_count(df):
return df.query('education == "a"').education.count()


def sum_mask(df):
return (df.education == 'a').sum()


def sum_mask_numpy(df):
return (df.education.values == 'a').sum()


def make_df(n):
L = list(string.ascii_letters)
df = pd.DataFrame(np.random.choice(L, size=n), columns=['education'])
return df


perfplot.show(
setup=make_df,
kernels=[shape, len_df, query_count, sum_mask, sum_mask_numpy],
n_range=[2**k for k in range(2, 25)],
logx=True,
logy=True,
equality_check=False,
xlabel='len(df)')
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Couple of ways using count or sum

In [338]: df
Out[338]:
col1 education
0    a       9th
1    b       9th
2    c       8th


In [335]: df.loc[df.education == '9th', 'education'].count()
Out[335]: 2


In [336]: (df.education == '9th').sum()
Out[336]: 2


In [337]: df.query('education == "9th"').education.count()
Out[337]: 2
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Try this: 

(df[education]=='9th').sum()
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An elegant way to count the occurrence of '?' or any symbol in any column, is to use built-in function isin of a dataframe object.

Suppose that we have loaded the 'Automobile' dataset into df object. We do not know which columns contain missing value ('?' symbol), so let do:

df.isin(['?']).sum(axis=0)

DataFrame.isin(values) official document says:

it returns boolean DataFrame showing whether each element in the DataFrame is contained in values

Note that isin accepts an iterable as input, thus we need to pass a list containing the target symbol to this function. df.isin(['?']) will return a boolean dataframe as follows.

    symboling   normalized-losses   make    fuel-type   aspiration-ratio ...
0   False       True                False   False       False
1   False       True                False   False       False
2   False       True                False   False       False
3   False       False               False   False       False
4   False       False               False   False       False
5   False       True                False   False       False
...

To count the number of occurrence of the target symbol in each column, let's take sum over all the rows of the above dataframe by indicating axis=0. The final (truncated) result shows what we expect:

symboling             0
normalized-losses    41
...
bore                  4
stroke                4
compression-ratio     0
horsepower            2
peak-rpm              2
city-mpg              0
highway-mpg           0
price                 4
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easy but not efficient:

list(df.education).count('9th')
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for finding a specific value of a column you can use the code below

irrespective of the preference you can use the any of the method you like

df.col_name.value_counts().Value_you_are_looking_for

take example of the titanic dataset

df.Sex.value_counts().male

this gives a count of all male on the ship Although if you want to count a numerical data then you cannot use the above method because value_counts() is used only with series type of data hence fails So for that you can use the second method example

the second method is

#this is an example method of counting on a data frame
df[(df['Survived']==1)&(df['Sex']=='male')].counts()

this is not that efficient as value_counts() but surely will help if you want to count values of a data frame hope this helps

EDIT -- If you wanna look for something with a space in between

you may use

df.country.count('united states') I believe this should solve the problem

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Simple example to count occurrences (unique values) in a column in Pandas data frame:

import pandas as pd


# URL to .csv file
data_url = 'https://yoursite.com/Arrests.csv'
# Reading the data
df = pd.read_csv(data_url, index_col=0)
# pandas count distinct values in column
df['education'].value_counts()

Outputs:

Education        47516
9th              41164
8th              25510
7th              25198
6th              25047
...
3rd                 2
2nd                 2
1st                 2
Name: name, Length: 190, dtype: int64
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I think this could be a more easy solution. Suppose you have the following data frame.

DATE        LANG        POSTS
2008-07-01  c#          3
2008-08-01  assembly    8
2008-08-01  javascript  2
2008-08-01  c           85
2008-08-01  python      11
2008-07-01  c#          3
2008-08-01  assembly    8
2008-08-01  javascript  62
2008-08-01  c           85
2008-08-01  python      14

you can find the occurrence of LANG item's sum like this

df.groupby('LANG').sum()

and you will have the sum of each individual language


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