构造熊猫数据框：视为列还是行？

Question

I find it hard to remember how Pandas decides to treat list as column or row at different times.

What is the general rule that is easy to remember?

Example:

pd.DataFrame(data=[['x','y','z'],['a','b','c']])

xyz
abc

pd.DataFrame(data={'A':['x','y','z'],'B':['a','b','c']})

xa
yb
zc

Answer 1

df = pd.DataFrame(data={'A':['x','y','z'],'B':['a','b','c']}) is best way of using pandas. This means 'A' is the column header and reference, ['x', 'y', 'z'] are values of that column.

You can even further filter rows etc. based on this like df[df['A']=='x'] to get only those rows which have value x in column 'A'

Answer 2

In second approach you can use DataFrame.from_dict with parameter orient :

a = pd.DataFrame.from_dict(data={'A':['x','y','z'],'B':['a','b','c']}, orient='index')
print (a)
   0  1  2
A  x  y  z
B  a  b  c

b= pd.DataFrame.from_dict(data={'A':['x','y','z'],'B':['a','b','c']}, orient='columns')
print (b)
   A  B
0  x  a
1  y  b
2  z  c

and first perfect explain EdChum in comment .