如何使用pandas计算数据框中每个日期的值？

Question

I'm very new to pandas library in python and I've been trying to piece together how to take a dataframe like this

    'Date'  'Color'
0   '05-10-2017'    'Red'
1   '05-10-2017'    'Green'
2   '05-10-2017'    'Blue'
3   '05-10-2017'    'Red'
4   '05-10-2017'    'Blue'
5   '05-11-2017'    'Red'
6   '05-11-2017'    'Green'
7   '05-11-2017'    'Red'
8   '05-11-2017'    'Green'
9   '05-11-2017'    'Blue'
10  '05-11-2017'    'Blue'
11  '05-11-2017'    'Red'
12  '05-11-2017'    'Blue'
13  '05-11-2017'    'Blue'
14  '05-12-2017'    'Green'
15  '05-12-2017'    'Blue'
16  '05-12-2017'    'Red'
17  '05-12-2017'    'Blue'
18  '05-12-2017'    'Blue'

and output one that has unique dates as an index, the colors as column headers the value count for each day like this:

'Date'       'Red' 'Green' 'Blue'
'05-10-2017'     2       1      2 
'05-11-2017'     3       2      3
'05-12-2017'     1       1      3

I have been struggling searching through this site for the past two days trying to piece together a way to achieve this and so far I have only been able to generate the index of unique dates. I'm having some trouble with value_counts. I would appreciate if anybody would be able to show me a method, or point me in the right direction if this has been answered already. I have exhausted my searching capabilities and have finally resolved to ask my first question ever on here. If I'm an idiot, please be gentle.

Answer 1

You can use:

1.

groupby + size for aggregting and unstack for reshape:

df1 = df.groupby(["'Date'","'Color'"]).size().unstack(fill_value=0)
print (df1)
'Color'       'Blue'  'Green'  'Red'
'Date'                              
'05-10-2017'       2        1      2
'05-11-2017'       4        2      3
'05-12-2017'       3        1      1

2.

pivot_table solution:

df1 = df.pivot_table(index="'Date'",columns="'Color'", aggfunc='size')
print (df1)
'Color'       'Blue'  'Green'  'Red'
'Date'                              
'05-10-2017'       2        1      2
'05-11-2017'       4        2      3
'05-12-2017'       3        1      1

3.

crosstab solution, slowier:

df1 = pd.crosstab(df["'Date'"],df["'Color'"])
print (df1)
'Color'       'Blue'  'Green'  'Red'
'Date'                              
'05-10-2017'       2        1      2
'05-11-2017'       4        2      3
'05-12-2017'       3        1      1