Если у меня есть такая таблица:

df = pd.DataFrame({
         'hID': [101, 102, 103, 101, 102, 104, 105, 101],
         'dID': [10, 11, 12, 10, 11, 10, 12, 10],
         'uID': ['James', 'Henry', 'Abe', 'James', 'Henry', 'Brian', 'Claude', 'James'],
         'mID': ['A', 'B', 'A', 'B', 'A', 'A', 'A', 'C']

I can do count(distinct hID) in Qlik to come up with count of 5 for unique hID. How do I do that in python using a pandas dataframe? Or maybe a numpy array? Similarly, if were to do count(hID) I will get 8 in Qlik. What is the equivalent way to do it in pandas?

Ответы (7)

Count distinct values, use nunique:


Count only non-null values, use count:


Count total values including null values, use the size attribute:


Edit to add condition

Use boolean indexing:


OR using query:

df.query('mID == "A"')['hID'].agg(['nunique','count','size'])


nunique    5
count      5
size       5
Name: hID, dtype: int64

If I assume data is the name of your dataframe, you can do :


this will show you the distinct element and their number of occurence.

I was looking for something similar and I found another way you may help you

  • If you want to count the number of null values, you could use this function:
def count_nulls(s):
    return s.size - s.count()
  • If you want to include NaN values in your unique counts, you need to pass dropna=False to the nunique function.
def unique_nan(s):
    return s.nunique(dropna=False)
  • Here is a summary of all the values together using the titanic dataset:
from scipy.stats import mode

agg_func_custom_count = {
    'embark_town': ['count', 'nunique', 'size', unique_nan, count_nulls, set]

You can find more info Here

You can use nunique in pandas:

# 5

you can use unique property by using len function

len(df['hID'].unique()) 5

Или получите количество уникальных значений для каждого столбца:


dID    3
hID    5
mID    3
uID    5
dtype: int64

Новое в pandas 0.20.0 pd.DataFrame.agg

df.agg(['count', 'size', 'nunique'])

         dID  hID  mID  uID
count      8    8    8    8
size       8    8    8    8
nunique    3    5    3    5

You've always been able to do an agg within a groupby. I used stack at the end because I like the presentation better.

df.groupby('mID').agg(['count', 'size', 'nunique']).stack()

             dID  hID  uID
A   count      5    5    5
    size       5    5    5
    nunique    3    5    5
B   count      2    2    2
    size       2    2    2
    nunique    2    2    2
C   count      1    1    1
    size       1    1    1
    nunique    1    1    1

To count unique values in column, say hID of dataframe df, use:


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