python - Plotting and GroupBy methods -

i learning take advantage of dataframes in pandas , use groupby methods produce plots of following:

i have 2 dataframes, 1 x-axis information , 1 y. in each dataframe there 3 versions of data, 'a', 'b', 'c'. need plot of y vs x each of (i.e. 3 lines).

example code:

df_x  <class 'pandas.core.frame.dataframe'> int64index: 100 entries, 0 99 data columns (total 3 columns):     100  non-null values b      100  non-null values c    100  non-null values dtypes: float64(2), object(1)  df_y  <class 'pandas.core.frame.dataframe'> int64index: 100 entries, 0 99 data columns (total 3 columns):     100  non-null values b      100  non-null values c    100  non-null values dtypes: float64(2), object(1) 

is there quick way produce desired plot avoiding loops , using pandas methods? i'm thinking of merging both frames , using groupby methods, don't know how go doing exactly.

thanks!

i think can plot directly using pyplot:

in [11]: plot(df_x, df_y)  # matplotlib.pyplot.plot out[11]: [<matplotlib.lines.line2d @ 0x109c02910>,  <matplotlib.lines.line2d @ 0x109c02b90>,  <matplotlib.lines.line2d @ 0x109c02ed0>] 

it seems need set legend after though:

pylab.legend(df_x.columns) 

if wanted reshape data form use .plot, perhaps use:

in [21]: df_x = pd.dataframe([[1,2,1],[2,3,4]], columns=list('abc'))  in [22]: df_y = pd.dataframe([[2,6,1],[4,9,4]], columns=list('abc'))  in [23]: pd.dataframe({'x': df_x.stack(), 'y': df_y.stack()}).reset_index(level=1).pivot('x', 'level_1', 'y') out[23]: level_1     b   c x 1         2 nan   1 2         4   6 nan 3       nan   9 nan 4       nan nan   4 

this going considerably less efficient (take lot more space needs to), since contains lot of missing data.