import numpy as np
import pandas as pd
obj2 = pd.series([4,7,-5,3],index=['d','b','a','c'])
obj2
out[99]:
d 4
b 7
a -5
c 3
dtype: int64
a =
b = pd.series(a)
bout[102]:
a 1
b 2
c 3
dtype: int64
in [172]: series_4['a']
out[172]: 4
series_4[['a','b']]
out[174]:
a 4
b 2
dtype: int64
in [175]: series_4[series_4>2]
out[175]:
a 4
c 3
dtype: int64
series_4
out[194]:
a 4
b 2
c 3
dtype: int64
series_4[:2]
out[195]:
a 4
b 2
dtype: int64
series_4['a':'c']
out[196]:
a 4
b 2
c 3
dtype: int64
注意刪除的是索引值
b
out[105]:
a 1
b 2
c 3
dtype: int64
b.drop('a')
out[106]:
b 2
c 3
dtype: int64
s1
out[112]:
a 1
b 2
c 3
dtype: int64
s1['a'] ='測試'
s1out[114]:
a 測試
b 2
c 3
dtype: object
s1
out[80]:
ceshi 1
001 3
002 5
003 6
004 8
dtype: int6
s1.index._values[0] = '測試's1
out[87]:
測試 1
001 3
002 5
003 6
004 8
dtype: int64
#列印s1
s1out[94]:
0 1.0
1 3.0
2 5.0
3 nan
4 6.0
5 8.0
dtype: float64
#將索引值為0的值重新賦值
s1[0] = 11111
s1out[96]:
0 11111.0
1 3.0
2 5.0
3 nan
4 6.0
5 8.0
dtype: float64
out[117]:
a 測試
b 2
c 3
0 11111
1 3
2 5
3 nan
4 6
5 8
dtype: object
s[0] ='改變了'
sout[121]:
0 改變了
1 3
2 5
3 nan
4 6
5 8
dtype: object
out[122]:
a 測試
b 2
c 3
0 改變了
1 3
2 5
3 nan
4 6
5 8
dtype: object
>>> data =
>>> data
>>> frame = dataframe(data)
>>> frame
pop state year
0 x 1 a
1 y 2 b
>>> dataframe(data,columns=['year','pop','state'])
year pop state
0 a x 1
1 b y 2
help(df.drop)
help on method drop in module pandas.core.generic:
drop(labels=none, axis=0, index=none, columns=none, level=none, inplace=false, errors='raise') method of pandas.core.frame.dataframe instance
return new object with labels in requested axis removed.
parameters
----------
labels : single label or list-like
index or column labels to drop.
axis : int or axis name
whether to drop labels from the index (0 / 'index') or
columns (1 / 'columns').
index, columns : single label or list-like
alternative to specifying `axis` (``labels, axis=1`` is
equivalent to ``columns=labels``).
.. versionadded:: 0.21.0
level : int or level name, default none
for multiindex
inplace : bool, default false
if true, do operation inplace and return none.
errors : , default 'raise'
if 'ignore', suppress error and existing labels are dropped.
returns
-------
dropped : type of caller
examples
--------
df = pd.dataframe(np.arange(12).reshape(3,4),
columns=['a', 'b', 'c', 'd'])
dfa b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
drop columns
df.drop(['b', 'c'], axis=1)
a d
0 0 3
1 4 7
2 8 11
###['b', 'c'])中。b和c中間有個空格
df.drop(columns=['b', 'c'])
a d
0 0 3
1 4 7
2 8 11
drop a row by index
df.drop([0, 1])
a b c d
2 8 9 10 11
notes
-----
specifying both `labels` and `index` or `columns` will raise a
valueerror.
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