2011年1月2日 · The currently selected solution produces incorrect results. To correctly solve this problem, we can perform a left-join from df1 to df2, making sure to first get just the unique rows for df2.

Just ask the question straight in plain English, hmm I mean in plain pandas. "Select all rows in df1 that are not in df2" translates to: df1[~df1.isin(df2).all(axis=1)] Out[127]: city1 city2 val 2 YYZ EWR 1 3 YYZ DFW 1 4 YYZ LAX 1 5 YYZ YYC 1

2018年2月26日 · Here is a possible solution: import pandas as pd df1 = pd.DataFrame({'id': [1, 2], 'name': ['A', 'B'], 'ex': ['A1', 'B1'], 'init': ['1,3,5,7,', '10,12,15,17,20 ...

So what I want is to check all df1 values against df2 column 1 - n and if there is a match with any value in df1 mark the index of df2 as True else it is False. python pandas

2016年3月21日 · Let's say I have a spark data frame df1, with several columns (among which the column id) and data frame df2 with two columns, id and other. Is there a way to replicate the following command: sqlContext.sql("SELECT df1.*, df2.other FROM df1 JOIN df2 ON df1.id = df2.id") by using only pyspark functions such as join(), select() and the like?

I want to remove all rows in df2 that are also in df1, but leave df1 unchanged. I am very close when using pd.concat() or merge(), but the problem is that I am creating a bunch of unnecessary columns [with concat() and merge()] and rows only found in df1 are added to df2 [with concat()].

2016年3月11日 · I think that pandas offers better alternatives to what you're suggesting (rationale below). For one, there's the pandas.Panel data structure, which was meant for things like you're doing here.

2016年5月20日 · To make it more generic of keeping both columns in df1 and df2:. import pyspark.sql.functions as F # Keep all columns in either df1 or df2 def outter_union(df1, df2): # Add missing columns to df1 left_df = df1 for column in set(df2.columns) - set(df1.columns): left_df = left_df.withColumn(column, F.lit(None)) # Add missing columns to df2 right_df = df2 for column in set(df1.columns) - set(df2 ...

df1=data.frame(a=rep(c(3000,4000,5000),each=4),b=c(50,60),c=1,as.list(colnames(mtcars))) df2=data.frame(a=c(3000,4000),b=60,c=c(1,2),as.list(LETTERS)) I want to select only the rows of df1 , where the combination of value for a and b is present in at least one row of df2 , without caring about c (the remaining common column).

2015年10月26日 · If you're on pandas < 0.17.0. You could work your way up like. In [182]: df = pd.merge(df1, df2, on='City', how='outer') In [183]: df Out[183]: City State_x State_y 0 Chicago Illinois Illinois 1 San Franciso California NaN 2 Boston Massachusett NaN 3 Mmmmiami NaN Florida 4 Dallas NaN Texas 5 Omaha NaN Nebraska In [184]: df.ix[df['State_y'].isnull(),:] Out[184]: City State_x State_y 1 San ...