dataframe

问题 I have recently begun looking at Dask for big data. I have a question on efficiently applying operations in parallel. Say I have some sales data like this: customerKey productKey transactionKey grossSales netSales unitVolume volume transactionDate ----------- -------------- ---------------- ---------- -------- ---------- ------ -------------------- 20353 189 219548 0.921058 0.921058 1 1 2017-02-01 00:00:00 2596618 189 215015 0.709997 0.709997 1 1 2017-02-01 00:00:00 30339435 189 215184 0

问题 I have recently begun looking at Dask for big data. I have a question on efficiently applying operations in parallel. Say I have some sales data like this: customerKey productKey transactionKey grossSales netSales unitVolume volume transactionDate ----------- -------------- ---------------- ---------- -------- ---------- ------ -------------------- 20353 189 219548 0.921058 0.921058 1 1 2017-02-01 00:00:00 2596618 189 215015 0.709997 0.709997 1 1 2017-02-01 00:00:00 30339435 189 215184 0

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and

问题 I am a new R user and new to StackOverflow. I will do my best to ask my question concisely and explicitly and my apologies if it is not communicated in the best way. I am working with two dataframes. I want to reorder the rows of one dataframe so that it is identical to the order of the rows in the second dataframe so I can add data from one to the other with their formats being the same. The column I want to reorder the rows according to is a column with character string identifiers of

问题 I am a new R user and new to StackOverflow. I will do my best to ask my question concisely and explicitly and my apologies if it is not communicated in the best way. I am working with two dataframes. I want to reorder the rows of one dataframe so that it is identical to the order of the rows in the second dataframe so I can add data from one to the other with their formats being the same. The column I want to reorder the rows according to is a column with character string identifiers of

问题 I am a new R user and new to StackOverflow. I will do my best to ask my question concisely and explicitly and my apologies if it is not communicated in the best way. I am working with two dataframes. I want to reorder the rows of one dataframe so that it is identical to the order of the rows in the second dataframe so I can add data from one to the other with their formats being the same. The column I want to reorder the rows according to is a column with character string identifiers of

问题 I have a dataframe (df) that looks like this: environment event time 2017-04-28 13:08:22 NaN add_rd 2017-04-28 08:58:40 NaN add_rd 2017-05-03 07:59:35 test add_env 2017-05-03 08:05:14 prod add_env ... Now my goal is for each add_rd in the event column, the associated NaN -value in the environment column should be replaced with a string RD . environment event time 2017-04-28 13:08:22 RD add_rd 2017-04-28 08:58:40 RD add_rd 2017-05-03 07:59:35 test add_env 2017-05-03 08:05:14 prod add_env ...

问题 I'm trying to compare two data frames with have same number of columns i.e. 4 columns with id as key column in both data frames df1 = spark.read.csv("/path/to/data1.csv") df2 = spark.read.csv("/path/to/data2.csv") Now I want to append new column to DF2 i.e. column_names which is the list of the columns with different values than df1 df2.withColumn("column_names",udf()) DF1 +------+---------+--------+------+ | id | |name | sal | Address | +------+---------+--------+------+ | 1| ABC | 5000 | US

问题 I'm trying to compare two data frames with have same number of columns i.e. 4 columns with id as key column in both data frames df1 = spark.read.csv("/path/to/data1.csv") df2 = spark.read.csv("/path/to/data2.csv") Now I want to append new column to DF2 i.e. column_names which is the list of the columns with different values than df1 df2.withColumn("column_names",udf()) DF1 +------+---------+--------+------+ | id | |name | sal | Address | +------+---------+--------+------+ | 1| ABC | 5000 | US