In [1]:

    

import pandas as pd


    

In [2]:

    

s = pd.Series([0, 1, 2], index=['a', 'b', 'c'])
print(s)


    

    




a    0
b    1
c    2
dtype: int64

In [3]:

    

df = pd.DataFrame(s)
print(df)


    

    




   0
a  0
b  1
c  2

In [4]:

    

print(type(df))


    

    




<class 'pandas.core.frame.DataFrame'>

In [5]:

    

df_ = pd.DataFrame([s])
print(df_)


    

    




   a  b  c
0  0  1  2

In [6]:

    

print(type(df_))


    

    




<class 'pandas.core.frame.DataFrame'>

In [7]:

    

s_name = pd.Series([0, 1, 2], index=['a', 'b', 'c'], name='X')
print(s_name)


    

    




a    0
b    1
c    2
Name: X, dtype: int64

In [8]:

    

print(pd.DataFrame(s_name))


    

    




   X
a  0
b  1
c  2

In [9]:

    

print(pd.DataFrame([s_name]))


    

    




   a  b  c
X  0  1  2

In [10]:

    

s1 = pd.Series([0, 1, 2], index=['a', 'b', 'c'])
print(s1)


    

    




a    0
b    1
c    2
dtype: int64

In [11]:

    

s2 = pd.Series([0.0, 0.1, 0.2], index=['a', 'b', 'c'])
print(s2)


    

    




a    0.0
b    0.1
c    0.2
dtype: float64

In [12]:

    

print(pd.DataFrame({'col0': s1, 'col1': s2}))


    

    




   col0  col1
a     0   0.0
b     1   0.1
c     2   0.2

In [13]:

    

print(pd.DataFrame([s1, s2]))


    

    




     a    b    c
0  0.0  1.0  2.0
1  0.0  0.1  0.2

In [14]:

    

print(pd.concat([s1, s2], axis=1))


    

    




   0    1
a  0  0.0
b  1  0.1
c  2  0.2

In [15]:

    

s1_name = pd.Series([0, 1, 2], index=['a', 'b', 'c'], name='X')
print(s1_name)


    

    




a    0
b    1
c    2
Name: X, dtype: int64

In [16]:

    

s2_name = pd.Series([0.0, 0.1, 0.2], index=['a', 'b', 'c'], name='Y')
print(s2_name)


    

    




a    0.0
b    0.1
c    0.2
Name: Y, dtype: float64

In [17]:

    

print(pd.DataFrame({s1_name.name: s1_name, s2_name.name: s2_name}))


    

    




   X    Y
a  0  0.0
b  1  0.1
c  2  0.2

In [18]:

    

print(pd.DataFrame([s1_name, s2_name]))


    

    




     a    b    c
X  0.0  1.0  2.0
Y  0.0  0.1  0.2

In [19]:

    

print(pd.concat([s1_name, s2_name], axis=1))


    

    




   X    Y
a  0  0.0
b  1  0.1
c  2  0.2

In [20]:

    

s3 = pd.Series([0.1, 0.2, 0.3], index=['b', 'c', 'd'])
print(s3)


    

    




b    0.1
c    0.2
d    0.3
dtype: float64

In [21]:

    

print(pd.DataFrame({'col0': s1, 'col1': s3}))


    

    




   col0  col1
a   0.0   NaN
b   1.0   0.1
c   2.0   0.2
d   NaN   0.3

In [22]:

    

print(pd.DataFrame([s1, s3]))


    

    




     a    b    c    d
0  0.0  1.0  2.0  NaN
1  NaN  0.1  0.2  0.3

In [23]:

    

print(pd.concat([s1, s3], axis=1))


    

    




     0    1
a  0.0  NaN
b  1.0  0.1
c  2.0  0.2
d  NaN  0.3






    




/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  """Entry point for launching an IPython kernel.

In [24]:

    

print(pd.concat([s1, s3], axis=1, join='inner'))


    

    




   0    1
b  1  0.1
c  2  0.2

In [25]:

    

print(s1.values)


    

    




[0 1 2]

In [26]:

    

print(type(s1.values))


    

    




<class 'numpy.ndarray'>

In [27]:

    

print(pd.DataFrame({'col0': s1.values, 'col1': s3.values}))


    

    




   col0  col1
0     0   0.1
1     1   0.2
2     2   0.3

In [28]:

    

print(pd.DataFrame([s1.values, s3.values]))


    

    




     0    1    2
0  0.0  1.0  2.0
1  0.1  0.2  0.3

In [29]:

    

# print(pd.concat([s1.values, s3.values], axis=1))
# TypeError: cannot concatenate object of type '<class 'numpy.ndarray'>'; only Series and DataFrame objs are valid


    

In [30]:

    

print(pd.DataFrame({'col0': s1, 'col1': s3.values}))


    

    




   col0  col1
a     0   0.1
b     1   0.2
c     2   0.3

In [31]:

    

print(pd.DataFrame([s1, s3.values]))


    

    




     a    b    c
0  0.0  1.0  2.0
1  NaN  NaN  NaN

In [32]:

    

print(pd.DataFrame({'col0': s1.values, 'col1': s3.values}, index=s1.index))


    

    




   col0  col1
a     0   0.1
b     1   0.2
c     2   0.3

In [33]:

    

print(pd.DataFrame([s1.values, s3.values], columns=s1.index))


    

    




     a    b    c
0  0.0  1.0  2.0
1  0.1  0.2  0.3

In [34]:

    

s4 = pd.Series([0.1, 0.2], index=['b', 'd'])
print(s4)


    

    




b    0.1
d    0.2
dtype: float64

In [35]:

    

print(pd.DataFrame({'col0': s1, 'col1': s4}))


    

    




   col0  col1
a   0.0   NaN
b   1.0   0.1
c   2.0   NaN
d   NaN   0.2

In [36]:

    

print(pd.DataFrame([s1, s4]))


    

    




     a    b    c    d
0  0.0  1.0  2.0  NaN
1  NaN  0.1  NaN  0.2

In [37]:

    

print(pd.concat([s1, s4], axis=1, join='inner'))


    

    




   0    1
b  1  0.1

In [38]:

    

# print(pd.DataFrame({'col0': s1.values, 'col1': s4.values}))
# ValueError: arrays must all be same length


    

In [39]:

    

print(pd.DataFrame([s1.values, s4.values]))


    

    




     0    1    2
0  0.0  1.0  2.0
1  0.1  0.2  NaN

In [40]:

    

s4.index = ['a', 'b']
print(s4)


    

    




a    0.1
b    0.2
dtype: float64

In [41]:

    

print(pd.DataFrame({'col0': s1, 'col1': s4}))


    

    




   col0  col1
a     0   0.1
b     1   0.2
c     2   NaN

In [42]:

    

print(pd.DataFrame({'col0': s1, 'col1': s4}).fillna(100))


    

    




   col0   col1
a     0    0.1
b     1    0.2
c     2  100.0

In [43]:

    

print(s)


    

    




a    0
b    1
c    2
dtype: int64

In [44]:

    

df = pd.DataFrame(s)
print(df)


    

    




   0
a  0
b  1
c  2

In [45]:

    

s[0] = 100
print(s)


    

    




a    100
b      1
c      2
dtype: int64

In [46]:

    

print(df)


    

    




     0
a  100
b    1
c    2

In [47]:

    

df_copy = pd.DataFrame(s, copy=True)
print(df_copy)


    

    




     0
a  100
b    1
c    2

In [48]:

    

s[1] = 100
print(s)


    

    




a    100
b    100
c      2
dtype: int64

In [49]:

    

print(df_copy)


    

    




     0
a  100
b    1
c    2

In [50]:

    

df_c = pd.concat([s1, s2], axis=1)
print(df_c)


    

    




   0    1
a  0  0.0
b  1  0.1
c  2  0.2

In [51]:

    

s1[0] = 100
print(s1)


    

    




a    100
b      1
c      2
dtype: int64

In [52]:

    

print(df_c)


    

    




   0    1
a  0  0.0
b  1  0.1
c  2  0.2

In [53]:

    

df_c_false = pd.concat([s1, s2], axis=1, copy=False)
print(df_c_false)


    

    




     0    1
a  100  0.0
b    1  0.1
c    2  0.2

In [54]:

    

s1[1] = 100
print(s1)


    

    




a    100
b    100
c      2
dtype: int64

In [55]:

    

print(df_c_false)


    

    




     0    1
a  100  0.0
b    1  0.1
c    2  0.2