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Pandas; how to concat two dataframes but only the columns that are the same?

If i have two DataFrames, EX:
df1 = pd.DataFrame({'Code':['1001','1002','1003','1004'],
'Place':['Chile','Peru','Colombia','Argentina']})
and:
df2 = pd.DataFrame({'Code':['1001','1002','1003'],
'Place':['Chile','Peru','Colombia']})
How can i concat this two to make one DataFrame of two rows but only with tha columns that are the same? Thanks

If I understand your question correctly, you want two rows, namely "Code" and "Place". ie. you need to transpose the DataFrame:
df = df1.merge(df2, how="inner").T
print(df)

Mapping Two dataframes Pandas

I want to map two dataframes in pandas , in DF1 I have
df1
my second dataframe looks like
df2
I want to merge the two dataframes and get something like this
merged DF
on the basis of the 1 occuring in the DF1 , it should be replaced by the value after merging
so far i have tried
mergedDF = pd.merge(df1,df2, on=companies)

Seems like you need .idxmax() method.
merged = df1.merge(df2, on='Company')
merged['values'] = merged[[x for x in merged.columns if x != 'Company']].idxmax(axis=1)

Get the missing columns from one dataframe and append it to another dataframe

I have a Dataframe df1 with the columns. I need to compare the headers of columns in df1 with a list of headers from df2
df1 =['a','b','c','d','f']
df2 =['a','b','c','d','e','f']
I need to compare the df1 with df2 and if any missing columns, I need to add them to df1 with blank values.
I tried concat and also append and both didn't work. with concat, I'm not able to add the column e and with append, it is appending all the columns from df1 and df2. How would I get only missing column added to df1 in the same order?
df1_cols = df1.columns
df2_cols = df2._combine_match_columns
if (df1_cols == df2_cols).all():
df1.to_csv(path + file_name, sep='|')
else:
print("something is missing, continuing")
#pd.concat([my_df,flat_data_frame], ignore_index=False, sort=False)
all_list = my_df.append(flat_data_frame, ignore_index=False, sort=False)
I wanted to see the results as
a|b|c|d|e|f - > headers
1|2|3|4||5 -> values

pandas.DataFrame.align
df1.align(df2, axis=1)[0]
By default this does an 'outer' join
By specifying axis=1 we focus on columns
This returns a tuple of both an aligned df1 and df2 with the calling dataframe being the first element. So I grab the first element with [0]
pandas.DataFrame.reindex
df1.reindex(columns=df1.columns | df2.columns)
You can treat pandas.Index objects like sets most of the time. So df1.columns | df2.columns is the union of those two index objects. I then reindex using the result.

Lets first create the two dataframes as:
import pandas as pd, numpy as np
df1 = pd.DataFrame(np.random.random((5,5)), columns = ['a','b','c','d','f'])
df2 = pd.DataFrame(np.random.random((5,7)), columns = ['a','b','c','d','e','f','g'])
Now add those columns of df2 to df1 (with nan values), which are not in df1:
for i in list(df2):
if i not in list(df1):
df1[i] = np.nan
Now display the columns of df1 alphabetically:
df1 = df1[sorted(list(df1))]

How to sum columns from three different dataframes with a common key

I am reading in an excel spreadsheet about schools with three sheets as follows.
import sys
import pandas as pd
inputfile = sys.argv[1]
xl = pd.ExcelFile(inputfile)
print xl.sheet_names
df1 = xl.parse(xl.sheet_names[0], skiprows=14)
df2 = xl.parse(xl.sheet_names[1], skiprows=14)
df3 = xl.parse(xl.sheet_names[2], skiprows=14)
df1.columns = [chr(65+i) for i in xrange(len(df1.columns))]
df2.columns = df1.columns
df3.columns = df1.columns
The unique id for each school is in column 'D' in each of the three dataframes. I would like to make a new dataframe which has two columns. The first is the sum of column 'G' from df1, df2, df3 and the second is the sum of column 'K' from df1, df2, df3. In other words, I think I need the following steps.
Filter rows for which unique column 'D' ids actually exist in all three dataframes. If the school doesn't appear in all three sheets then I discard it.
For each remaining row (school), add up the values in column 'G' in the three dataframes.
Do the same for column 'K'.
I am new to pandas but how should I do this? Somehow the unique ids have to be used in steps 2 and 3 to make sure the values that are added correspond to the same school.
Attempted solution
df1 = df1.set_index('D')
df2 = df2.set_index('D')
df3 = df3.set_index('D')
df1['SumK']= df1['K'] + df2['K'] + df3['K']
df1['SumG']= df1['G'] + df2['G'] + df3['G']

After concatenating the dataframes, you can use groupby and count to get a list of values for "D" that exist in all three dataframes since there is only one in each dataframe. You can then use this to filter concatenated dataframe to sum whichever columns you need, e.g.:
df = pd.concat([df1, df2, df3])
criteria = df.D.isin((df.groupby('D').count() == 3).index)
df[criteria].groupby('D')[['G', 'K']].sum()

pandas combine_first with particular index columns?

I'm trying to join two dataframes in pandas to have the following behavior: I want to join on a specified column, but have it so redundant columns are not added to the dataframe. This is analogous to combine_first except combine_first does not seem to take an index column optional argument. Example:
# combine df1 and df2 based on "id" column
df1 = pandas.merge(df2, how="outer", on=["id"])
The problem with the above is that columns common to df1/df2 aside from "id" will be added twice (with _x,_y prefixes) to df1. How can I do something like:
# Do outer join from df2 to df1, matching items by "id" but not adding
# columns that are redundant (df1 takes precedence if the values disagree)
df1.combine_first(df2, on=["id"])
How can this be done?

If you are trying to merge columns from df2 into df1 while excluding any redundant columns, the following should work.
df1.set_index("id", inplace=True)
df2.set_index("id", inplace=True)
df3 = df1.merge(df2.ix[:,df2.columns-df1.columns], left_index=True, right_index=True, how="outer")
However this obviously will not update any values from df1 with values from df2 as it is only bringing in non-redundant columns. But since you said df1 will take precedence on any values that disagree, perhaps this will do the trick?