How to rotate a single column containing rows in a data frame?

Question

I am trying to change the shape of pandas data by turning one of the columns in the data into rows (by rotation or inaction).

I'm new to this, so I'll probably miss something obvious. I searched many times, but could not successfully apply any solutions that I came across.

df
    Location    Month       Metric       Value
0   Texas       January     Temperature  10
1   New York    January     Temperature  20
2   California  January     Temperature  30
3   Alaska      January     Temperature  40
4   Texas       January     Color        Red
5   New York    January     Color        Blue
6   California  January     Color        Green
7   Alaska      January     Color        Yellow
8   Texas       February    Temperature  15
9   New York    February    Temperature  25
10  California  February    Temperature  35
11  Alaska      February    Temperature  NaN
12  Texas       February    Color        NaN
13  New York    February    Color        Purple
14  California  February    Color        Orange
15  Alaska      February    Color        Brown

I am trying to "expand" Metric values ​​into columns. The final goal is the result:

Location    Month     Temperature   Color
Texas       January   10            Red
New York    January   20            Blue
California  January   30            Green
Alaska      January   40            Yellow
Texas       February  15    
New York    February  25            Purple
California  February  35            Orange
Alaska      February                Brown

I tried using pivot, pivot_table, as well as stack methods, but I'm sure something is missing. Many of the complications seem to be due to the fact that I mix strings with numbers and also have some missing values ​​in the data.

This is the closest that I managed to get so far, but I do not want extra rows for the month column, which leads to more empty values:

df.set_index(['Location','Month','Metric'], append=True, inplace=True)
df.unstack()

    Value
    Metric              Color   Temperature
    Location    Month       
0   Texas       January None    10
1   New York    January None    20
2   California  January None    30
3   Alaska      January None    40
4   Texas       January Red     None
5   New York    January Blue    None
6   California  January Green   None
7   Alaska      January Yellow  None

. , , , .

Answer 1

, . - , -

Metric                Color Temperature
Location   Month                       
Alaska     February   Brown         NaN
           January   Yellow          40
California February  Orange          35
           January    Green          30
New York   February  Purple          25
           January     Blue          20
Texas      February     NaN          15
           January      Red          10

-

df_p = df.pivot_table(index=['Location', 'Month'], columns=['Metric'], values='Value', aggfunc=np.sum)

Answer 2

Month , set_index unstack:

#add another months
cats = ['January','February']
df['Month'] = pd.Categorical(df['Month'], categories=cats, ordered=True)

, , @asongtoruin:

df['Month'] = pd.Categorical(df['Month'], categories=df['Month'].unique(), ordered=True)

---

df = (df.set_index(['Location','Month','Metric'])['Value']
       .unstack()
       .reset_index()
       .rename_axis(None, axis=1)
       .sort_values('Month'))
print (df)
     Location     Month   Color Temperature
0      Alaska   January  Yellow          40
2  California   January   Green          30
4    New York   January    Blue          20
6       Texas   January     Red          10
1      Alaska  February   Brown         NaN
3  California  February  Orange          35
5    New York  February  Purple          25
7       Texas  February     NaN          15