We Keep Coding

Pandas Use a column consists of column names to populate the values dynamically into another column

I would like to obtain the 'Value' column below, from the original df:
A B C Column_To_Use
0 2 3 4 A
1 5 6 7 C
2 8 0 9 B
A B C Column_To_Use Value
0 2 3 4 A 2
1 5 6 7 C 7
2 8 0 9 B 0

Use DataFrame.lookup:
df['Value'] = df.lookup(df.index, df['Column_To_Use'])
print (df)
A B C Column_To_Use Value
0 2 3 4 A 2
1 5 6 7 C 7
2 8 0 9 B 0

Python - Pandas, split long column to multiple columns

Given the following DataFrame:
>>> pd.DataFrame(data=[['a',1],['a',2],['b',3],['b',4],['c',5],['c',6],['d',7],['d',8],['d',9],['e',10]],columns=['key','value'])
key value
0 a 1
1 a 2
2 b 3
3 b 4
4 c 5
5 c 6
6 d 7
7 d 8
8 d 9
9 e 10
I'm looking for a method that will change the structure based on the key value, like so:
a b c d e
0 1 3 5 7 10
1 2 4 6 8 10 <- 10 is duplicated
2 2 4 6 9 10 <- 10 is duplicated
The result row number is as the longest group count (d in the above example) and the missing values are duplicates of the last available value.

Create MultiIndex by set_index with counter column by cumcount, reshape by unstack, repalce missing values by last non missing ones with ffill and last converting all data to integers if necessary:
df = df.set_index([df.groupby('key').cumcount(),'key'])['value'].unstack().ffill().astype(int)
Another solution with custom lambda function:
df = (df.groupby('key')['value']
.apply(lambda x: pd.Series(x.values))
.unstack(0)
.ffill()
.astype(int))
print (df)
key a b c d e
0 1 3 5 7 10
1 2 4 6 8 10
2 2 4 6 9 10

Using pivot , with groupby + cumcount
df.assign(key2=df.groupby('key').cumcount()).pivot('key2','key','value').ffill().astype(int)
Out[214]:
key a b c d e
key2
0 1 3 5 7 10
1 2 4 6 8 10
2 2 4 6 9 10

Reshape MultiIndex dataframe to tabular format

Given a sample MultiIndex:
idx = pd.MultiIndex.from_product([[0, 1, 2], ['a', 'b', 'c', 'd']])
df = pd.DataFrame({'value' : np.arange(12)}, index=idx)
df
value
0 a 0
b 1
c 2
d 3
1 a 4
b 5
c 6
d 7
2 a 8
b 9
c 10
d 11
How can I efficiently convert this to a tabular format like so?
a b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Furthermore, given the dataframe above, how can I bring it back to its original multi-indexed state?
What I've tried:
pd.DataFrame(df.values.reshape(-1, df.index.levels[1].size),
index=df.index.levels[0], columns=df.index.levels[1])
Which works for the first problem, but I'm not sure how to bring it back to its original from there.

Using unstack and stack
In [5359]: dff = df['value'].unstack()
In [5360]: dff
Out[5360]:
a b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
In [5361]: dff.stack().to_frame('name')
Out[5361]:
name
0 a 0
b 1
c 2
d 3
1 a 4
b 5
c 6
d 7
2 a 8
b 9
c 10
d 11

By using get_level_values
pd.crosstab(df.index.get_level_values(0),df.index.get_level_values(1),values=df.value,aggfunc=np.sum)
Out[477]:
col_0 a b c d
row_0
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11

Another alternative, which you should think of when using stack/unstack (though unstack is clearly better in this case!) is pivot_table:
In [11]: df.pivot_table(values="value", index=df.index.get_level_values(0), columns=df.index.get_level_values(1))
Out[11]:
a b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11

Finding the maximum entry based on another column in a data frame

Suppose I have a data frame with 3 columns: A, B, C. I want to group by column A, and find the row (for each unique A) with the maximum entry in C, so that I can store that row.A, row.B, row.C into a dictionary elsewhere.
What's the best way to do this without using iterrows?

# generate sample data
import pandas as pd
df = pd.DataFrame(np.random.randint(0,10,(10,3)))
df.columns = ['A','B','C']
# sort by C, group by A, take last row of each group
df.sort('C').groupby('A').nth(-1)

Here's another method. If df is the DataFrame, you can write df.groupby('A').apply(lambda d: d.ix[d['C'].argmax()]).
For example,
In [96]: df
Out[96]:
A B C
0 1 0 3
1 3 0 4
2 0 4 5
3 2 4 0
4 3 1 1
5 1 6 2
6 3 6 0
7 4 0 1
8 2 3 4
9 0 5 0
10 7 6 5
11 3 1 2
In [97]: g = df.groupby('A').apply(lambda d: d['C'].argmax())
In [98]: g
Out[98]:
A
0 2
1 0
2 8
3 1
4 7
7 10
dtype: int64
In [99]: df.ix[g.values]
Out[99]:
A B C
2 0 4 5
0 1 0 3
8 2 3 4
1 3 0 4
7 4 0 1
10 7 6 5

Concatenate Two DataFrames With Hierarchical Columns

I would like to merge two DataFrames while creating a multilevel column naming scheme denoting which dataframe the rows came from. For example:
In [98]: A=pd.DataFrame(np.arange(9.).reshape(3,3),columns=list('abc'))
In [99]: A
Out[99]:
a b c
0 0 1 2
1 3 4 5
2 6 7 8
In [100]: B=A.copy()
If I use pd.merge(), then I get
In [104]: pd.merge(A,B,left_index=True,right_index=True)
Out[104]:
a_x b_x c_x a_y b_y c_y
0 0 1 2 0 1 2
1 3 4 5 3 4 5
2 6 7 8 6 7 8
Which is what I expect with that statement, what I would like (but I don't know how to get!) is:
In [104]: <<one or more statements>>
Out[104]:
A B
a b c a b c
0 0 1 2 0 1 2
1 3 4 5 3 4 5
2 6 7 8 6 7 8
Can this be done without changing the original pd.DataFrame calls? I am reading the data in the dataframes in from .csv files and that might be my problem.

first case can be ordered arbitrarily among A,B (not the columns, just the order A or B)
2nd should preserve ordering
IMHO this is pandonic!
In [5]: concat(dict(A = A, B = B),axis=1)
Out[5]:
A B
a b c a b c
0 0 1 2 0 1 2
1 3 4 5 3 4 5
2 6 7 8 6 7 8
In [6]: concat([ A, B ], keys=['A','B'],axis=1)
Out[6]:
A B
a b c a b c
0 0 1 2 0 1 2
1 3 4 5 3 4 5
2 6 7 8 6 7 8

Here's one way, which does change A and B:
In [10]: from itertools import cycle
In [11]: A.columns = pd.MultiIndex.from_tuples(zip(cycle('A'), A.columns))
In [12]: A
Out[12]:
A
a b c
0 0 1 2
1 3 4 5
2 6 7 8
In [13]: B.columns = pd.MultiIndex.from_tuples(zip(cycle('B'), B.columns))
In [14]: A.join(B)
Out[14]:
A B
a b c a b c
0 0 1 2 0 1 2
1 3 4 5 3 4 5
2 6 7 8 6 7 8
I actually think this would be a good alternative behaviour, rather than suffixes...