1吭露、官方文檔
ndarray.size
Number of elements in the array.矩陣中元素的個數(shù)计露。
s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4
2蔫敲、size包括NaN值，count不包括：
In [46]:
df = pd.DataFrame({'a':[0,0,1,2,2,2], 'b':[1,2,3,4,np.NaN,4], 'c':np.random.randn(6)})
df
Out[46]:
a b c
0 0 1 1.067627
1 0 2 0.554691
2 1 3 0.458084
3 2 4 0.426635
4 2 NaN -2.238091
5 2 4 1.256943
In [48]:
print(df.groupby(['a'])['b'].count())
print(df.groupby(['a'])['b'].size())
a
0 2
1 1
2 2
Name: b, dtype: int64
a
0 2
1 1
2 3
dtype: int64
3引矩、即使數(shù)據(jù)沒有NA值梁丘，count（）的結(jié)果也更加冗長
In [114]:
grouped = fec_mrbo.groupby(['cand_nm',labels])
grouped.size().unstack(0)
Out[114]:
cand_nm Obama, Barack Romney, Mitt
contb_receipt_amt
(0, 1] 493.0 77.0
(1, 10] 40070.0 3681.0
(10, 100] 372280.0 31853.0
(100, 1000] 153991.0 43357.0
(1000, 10000] 22284.0 26186.0
(10000, 100000] 2.0 1.0
(100000, 1000000] 3.0 NaN
(1000000, 10000000] 4.0 NaN
In [115]:
grouped = fec_mrbo.groupby(['cand_nm',labels])
grouped.count().unstack(0)
Out[115]:
cmte_id cand_id contbr_nm contbr_city contbr_st ... memo_cd memo_text form_tp file_num parties
cand_nm Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt ... Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt Obama, Barack Romney, Mitt
contb_receipt_amt
(0, 1] 493.0 77.0 493.0 77.0 493.0 77.0 493.0 77.0 493.0 77.0 ... 31.0 1.0 138.0 10.0 493.0 77.0 493.0 77.0 493.0 77.0
(1, 10] 40070.0 3681.0 40070.0 3681.0 40070.0 3681.0 40070.0 3681.0 40070.0 3681.0 ... 4645.0 14.0 4781.0 53.0 40070.0 3681.0 40070.0 3681.0 40070.0 3681.0
(10, 100] 372280.0 31853.0 372280.0 31853.0 372280.0 31853.0 372276.0 31853.0 372280.0 31853.0 ... 33331.0 74.0 33789.0 236.0 372280.0 31853.0 372280.0 31853.0 372280.0 31853.0
(100, 1000] 153991.0 43357.0 153991.0 43357.0 153991.0 43357.0 153991.0 43355.0 153987.0 43357.0 ... 31674.0 347.0 31897.0 849.0 153991.0 43357.0 153991.0 43357.0 153991.0 43357.0
(1000, 10000] 22284.0 26186.0 22284.0 26186.0 22284.0 26186.0 22284.0 26185.0 22284.0 26186.0 ... 16622.0 640.0 16693.0 2217.0 22284.0 26186.0 22284.0 26186.0 22284.0 26186.0
(10000, 100000] 2.0 1.0 2.0 1.0 2.0 1.0 2.0 1.0 2.0 1.0 ... 0.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 2.0 1.0
(100000, 1000000] 3.0 NaN 3.0 NaN 3.0 NaN 3.0 NaN 3.0 NaN ... 3.0 NaN 3.0 NaN 3.0 NaN 3.0 NaN 3.0 NaN
(1000000, 10000000] 4.0 NaN 4.0 NaN 4.0 NaN 4.0 NaN 4.0 NaN ... 4.0 NaN 4.0 NaN 4.0 NaN 4.0 NaN 4.0 NaN
