naturallyyou could have computed this yourself by boolean indexingin [ ]data[data notnull()out[ ] with dataframe objectsthese are bit more complex you may want to drop rows or columns which are all na or just those containing any nas dropna by default drops any row containing missing valuein [ ]data dataframe([[ ][ nana][nanana][na ]]in [ ]cleaned data dropna(in [ ]data out[ ] nan nan nan nan nan nan in [ ]cleaned out[ ] passing how='allwill only drop rows that are all nain [ ]data dropna(how='all'out[ ] nan nan nan dropping columns in the same way is only matter of passing axis= in [ ]data[ na in [ ]data out[ ] nan nan nan nan nan nan nan nan nan nan in [ ]data dropna(axis= how='all'out[ ] nan nan nan nan nan nan related way to filter out dataframe rows tends to concern time series data suppose you want to keep only rows containing certain number of observations you can indicate this with the thresh argumentin [ ]df dataframe(np random randn( )in [ ]df ix[: nadf ix[: na getting started with pandas www it-ebooks info