――It's just a memo, but I'll summarize it easily. --I will update it from time to time (probably)
In [1]: df = read_csv('./input/hoge.csv')
--It's easy to forget single quotes. ..
In [2]: df
Out[3]:
label a b c d
0 aa 1 11 111 e
1 bb 2 22 222 e
2 cc 3 33 333 e
3 dd 4 44 444 e
In [2]: df[[1]]
Out[2]:
a
0 1
1 2
2 3
3 4
In [3]: df['a']
Out[3]:
0 1
1 2
2 3
3 4
Name: a, dtype: int64
In [4]: df.loc[:,['a','b']]
Out[4]:
a b
0 1 11
1 2 22
2 3 33
3 4 44
In [5]: df.iloc[:,[1,2]]
Out[5]:
a b
0 1 11
1 2 22
2 3 33
3 4 44
In [6]: df.describe()
Out[6]:
a b c
count 4.000000 4.000000 4.000000
mean 2.500000 27.500000 277.500000
std 1.290994 14.200939 143.300384
min 1.000000 11.000000 111.000000
25% 1.750000 19.250000 194.250000
50% 2.500000 27.500000 277.500000
75% 3.250000 35.750000 360.750000
max 4.000000 44.000000 444.000000
In [7]: df.values
Out[7]:
array([['aa', 1, 11, 111, 'e'],
['bb', 2, 22, 222, 'e'],
['cc', 3, 33, 333, 'e'],
['dd', 4, 44, 444, 'e']], dtype=object)
>>> df.dtypes
target int64
v1 float64
v2 float64
v3 object
v4 float64
v5 float64
v6 float64
v7 float64
v8 int64
v9 float64
dtype: object
>>> df.ix[:, df.dtypes == np.int64]
target v8
No.1 1 2
No.2 2 2
iteritems(), iterrows()
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['a', 'b', 'c'])
>>> df
A B C
a 1 4 a
b 2 5 b
c 3 6 c
>>> for (key,column) in df.iteritems():
print key
print column
A
a 1
b 2
c 3
Name: A, dtype: int64
B
a 4
b 5
c 6
Name: B, dtype: int64
C
a x
b y
c z
Name: C, dtype: object
>>> for (key, row) in df.iterrows():
print key
print row
a
A 1
B 4
C x
Name: a, dtype: object
b
A 2
B 5
C y
Name: b, dtype: object
c
A 3
B 6
C z
Name: c, dtype: object
factorize
>>> pd.factorize(df['A'])
(array([0, 1, 2]), Int64Index([1, 2, 3], dtype='int64'))
>>> pd.factorize(df['B'])
(array([0, 1, 2]), Int64Index([4, 5, 6], dtype='int64'))
>>> pd.factorize(df['C'])
(array([0, 1, 2]), Index([u'x', u'y', u'z'], dtype='object'))
>>> df['C'], indexer = pd.factorize(df['C'])
>>> df
A B C
a 1 4 0
b 2 5 1
c 3 6 2
>>> indexer
Index([u'x', u'y', u'z'], dtype='object')
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