pandas Data Structures - DataFrame

A DataFrame represents a rectangular table of data and contains an ordered collec‐
tion of columns, each of which can be a different value type (numeric, string,
boolean, etc.).

It can be thought of
as a dict of Series all sharing the same index.

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002, 2003],
'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
frame = pd.DataFrame(data)

In [45]: frame
Out[45]:
pop state year
0 1.5 Ohio 2000
1 1.7 Ohio 2001
2 3.6 Ohio 2002
3 2.4 Nevada 2001
4 2.9 Nevada 2002
5 3.2 Nevada 2003

In [47]: pd.DataFrame(data, columns=['year', 'state', 'pop'])
Out[47]:
year state pop
0 2000 Ohio 1.5
1 2001 Ohio 1.7
2 2002 Ohio 3.6
3 2001 Nevada 2.4
4 2002 Nevada 2.9
5 2003 Nevada 3.2

In [51]: frame2['state']
Out[51]:
one Ohio
two Ohio
three Ohio
four Nevada
five Nevada
six Nevada
Name: state, dtype: object

In [52]: frame2.year
Out[52]:
one 2000
two 2001
three 2002
four 2001
five 2002
six 2003
Name: year, dtype: int64

Note that the returned Series have the same index as the DataFrame, and their name
attribute has been appropriately set.

Rows can also be retrieved by position or name with the special loc attribute.

In [53]: frame2.loc['three']
Out[53]:
year 2002
state Ohio
pop 3.6
debt NaN
Name: three, dtype: object

In [54]: frame2['debt'] = 16.5
In [55]: frame2
Out[55]:
year state pop debt
one 2000 Ohio 1.5 16.5
two 2001 Ohio 1.7 16.5
three 2002 Ohio 3.6 16.5
four 2001 Nevada 2.4 16.5
five 2002 Nevada 2.9 16.5
six 2003 Nevada 3.2 16.5
In [56]: frame2['debt'] = np.arange(6.)
In [57]: frame2
Out[57]:
year state pop debt
one 2000 Ohio 1.5 0.0
two 2001 Ohio 1.7 1.0
three 2002 Ohio 3.6 2.0
four 2001 Nevada 2.4 3.0
five 2002 Nevada 2.9 4.0
six 2003 Nevada 3.2 5.0

In [58]: val = pd.Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])
In [59]: frame2['debt'] = val
In [60]: frame2
Out[60]:
year state pop debt
one 2000 Ohio 1.5 NaN
two 2001 Ohio 1.7 -1.2
three 2002 Ohio 3.6 NaN
four 2001 Nevada 2.4 -1.5
five 2002 Nevada 2.9 -1.7
six 2003 Nevada 3.2 NaN

Another common form of data is a nested dict of dicts:

In [65]: pop = {'Nevada': {2001: 2.4, 2002: 2.9},
....: 'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}

If the nested dict is passed to the DataFrame, pandas will interpret the outer dict keys
as the columns and the inner keys as the row indices:

In [66]: frame3 = pd.DataFrame(pop)
In [67]: frame3
Out[67]:
Nevada Ohio
2000 NaN 1.5
2001 2.4 1.7
2002 2.9 3.6

In [80]: labels = pd.Index(np.arange(3))
In [81]: labels
Out[81]: Int64Index([0, 1, 2], dtype='int64')
In [82]: obj2 = pd.Series([1.5, -2.5, 0], index=labels)
In [83]: obj2
Out[83]:
0 1.5
1 -2.5
2 0.0
dtype: float64
In [84]: obj2.index is labels
Out[84]: True