Abstract:
Create data frame using different approach. Insert or deletion
column/row of a data frame
Three methods for
creating a data frame are present here.
>>>
print "\ncreate a data frame using a dictionary of Series
objects:"
create
a data frame using a dictionary of Series objects:
>>>
df = pd.DataFrame(np.random.rand(3,4), columns=list("ABCD"),
index=list("abc"))
>>>
print df
A B C D
a
0.012619 0.410888 0.684326 0.303347
b
0.683163 0.300420 0.438949 0.076538
c
0.323952 0.483425 0.195125 0.764243
[3
rows x 4 columns]
>>>
>>>
print '\ncreate a data frame using a dictionary of ndarrays:'
create
a data frame using a dictionary of ndarrays:
>>>
df=pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]})
>>>
df.index=['a','b','c']
>>>
print df
A B C
a
1 4 7
b
2 5 8
c
3 6 9
[3
rows x 3 columns]
>>>
>>>
print '\ncreate a data frame using a structured dictionary:'
create
a data frame using a structured dictionary:
>>>
data=np.zeros((3,),
dtype=[('name','a15'),('age','a15'),('weight','f4')] )
>>>
data[:]=[('a', 34,150),('b',23,170),('c',89,146)]
>>>
df=pd.DataFrame(data)
>>>
print df
name age weight
0
a 34 150
1
b 23 170
2
c 89 146
[3
rows x 3 columns]
>>>
Then, the function
del() and pop() can delete a column:
>>>
print df
A B C D
a
0.249442 0.802315 0.600084 0.364948
b
0.337858 0.914759 0.980284 0.179295
c
0.992502 0.287009 0.439516 0.971466
[3
rows x 4 columns]
>>>
del df['A']
>>>
print 'delete a column using del:', df
delete
a column using del:
B C D
a
0.802315 0.600084 0.364948
b
0.914759 0.980284 0.179295
c
0.287009 0.439516 0.971466
[3
rows x 3 columns]
>>>
df.pop('C')
>>>
a 0.600084
b
0.980284
c
0.439516
Name:
C, dtype: float64
>>>
print 'delete a column using pop():', df
delete
a column using pop():
B D
a
0.802315 0.364948
b
0.914759 0.179295
c
0.287009 0.971466
[3
rows x 2 columns]
>>>
df=df.ix[:,1:]
>>>
print 'delete the first column using index:', df
delete
the first column using index: D
a
0.364948
b
0.179295
c
0.971466
[3
rows x 1 columns]
>>>
print df
B D
a
0.399407 0.862440
b
0.940136 0.470817
c
0.074161 0.638882
[3
rows x 2 columns]
And the drop() can delete a row.
>>>
df.drop('a')
B D
b
0.940136 0.470817
c
0.074161 0.638882
[2
rows x 2 columns]
>>>
print 'delete a row using drop():', df
delete
a row using drop(): df=df[1:]
B D
a
0.399407 0.862440
b
0.940136 0.470817
c
0.074161 0.638882
[3
rows x 2 columns]
>>>
>>> print 'delete the first row:', df
delete
the first row: B D
b
0.940136 0.470817
c
0.074161 0.638882
[2
rows x 2 columns]
>>>
And, the function of
insert() can insert a column into a data frame.
>>>
df = pd.DataFrame(np.random.rand(3,4), columns=list("ABCD"),
index=list("abc"))
>>>
df.insert(1, 'E', [245,7,2])
>>>
print 'insert a column using insert():', df
insert
a column using insert():
A
E B C D
a
0.545596 245 0.511448 0.856543 0.829688
b
0.377887 7 0.769014 0.572489 0.301208
c
0.296155 2 0.407141 0.778476 0.819889
[3
rows x 5 columns]
>>>
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