dataframe

问题 I am trying to fetch rows from a lookup table (3 rows and 3 columns) and iterate row by row and pass values in each row to a SPARK SQL as parameters. DB | TBL | COL ---------------- db | txn | ID db | sales | ID db | fee | ID I tried this in spark shell for one row, it worked. But I am finding it difficult to iterate over rows. val sqlContext = new org.apache.spark.sql.SQLContext(sc) val db_name:String = "db" val tbl_name:String = "transaction" val unique_col:String = "transaction_number" val

问题 When I run the line below, the NaN number in the dataframe does not get modified. Utilizing the exact same argument with .to_csv() , I get the expected result. Does .to_html require something different? df.to_html('file.html', float_format='{0:.2f}'.format, na_rep="NA_REP") 回答1: It looks like the float_format doesn't play nice with na_rep . However, you can work around it if you pass a function to float_format that conditionally handles your NaNs along with the float formatting you want: >>>

问题 Suppose that I have two data frames df1 = data.frame(x=1:10) df2 = data.frame(x=11:20) and I want a scatter plot with these two series defining the coordinates. It would be simple to do plot(df1$x,df2$x) From what I can tell so far about ggplot2, I could also do df = data.frame(x1 = df1$x, x2 = df2$x) ggplot(data = df, aes(x=x1, y=x2)) + geom_point() rm(df) but that would be slower (for me) than not creating a new data frame, is hard to read, and could lead to increased mistakes (deleting the

问题 I have multiple pandas dataframes, to keep it simple, let's say I have three. >> df1= col1 col2 id1 A B id2 C D id3 B A id4 E F >> df2= col1 col2 id1 B A id2 D C id3 M N id4 F E >> df3= col1 col2 id1 A B id2 D C id3 N M id4 E F The result needed is : >> df= col1 col2 id1 A B id2 C D id3 E F Because the pairs (A, B),(C, D),(E, F) appear in all the data frames although it may be reversed. While using pandas merge it just considers the way columns are passed. To check my observation I tried the

问题 I'm importing some csv data into a Pandas DataFrame (in Python). One series is meant to be all numerical values. However, it also contains some spurious "$-" elements represented as strings. These have been left over from previous formatting. If I just import the series, Pandas reports it as a series of 'object'. What's the best way to replace these "$-" strings with zeros? Or more generally, how can I replace all the strings in a series (which is predominantly numerical), with a numerical

问题 I have a dataframe like this: id date company ...... 123 2019-01-01 A 224 2019-01-01 B 345 2019-01-01 B 987 2019-01-03 C 334 2019-01-03 C 908 2019-01-04 C 765 2019-01-04 A 554 2019-01-05 A 482 2019-01-05 D and I want to get the cumulative number of unique values over time for the 'company' column. So if a company appears at a later date they are not counted again. My expected output is: date cumulative_count 2019-01-01 2 2019-01-03 3 2019-01-04 3 2019-01-05 4 I've tried: df.groupby(['date'])

问题 I have a dataframe like this: id date company ...... 123 2019-01-01 A 224 2019-01-01 B 345 2019-01-01 B 987 2019-01-03 C 334 2019-01-03 C 908 2019-01-04 C 765 2019-01-04 A 554 2019-01-05 A 482 2019-01-05 D and I want to get the cumulative number of unique values over time for the 'company' column. So if a company appears at a later date they are not counted again. My expected output is: date cumulative_count 2019-01-01 2 2019-01-03 3 2019-01-04 3 2019-01-05 4 I've tried: df.groupby(['date'])

问题 How can we replace missing values with 0.0 for a column in a DataFrame ? 回答1: There are a few different approaches to this problem (valid for Julia 1.x): Base.replace! Probably the easiest approach is to use replace! or replace from base Julia. Here is an example with replace! : julia> using DataFrames julia> df = DataFrame(x = [1, missing, 3]) 3×1 DataFrame │ Row │ x │ │ │ Int64⍰ │ ├─────┼─────────┤ │ 1 │ 1 │ │ 2 │ missing │ │ 3 │ 3 │ julia> replace!(df.x, missing => 0); julia> df 3×1

问题 I am getting strange error while saving dataframe to AWS S3. df.coalesce(1).write.mode(SaveMode.Overwrite) .json(s"s3://myawsacc/results/") In the same location I was able to insert the data from spark-shell . and is working... spark.sparkContext.parallelize(1 to 4).toDF.write.mode(SaveMode.Overwrite) .format("com.databricks.spark.csv") .save(s"s3://myawsacc/results/") My question is why its working in spark-shell and is not working via spark-submit ? Is there any logic/properties

问题 I am struggling with MultiIndex DataFrame in python pandas. Suppose I have a df like this: count day group name A Anna 10 Monday Beatrice 15 Tuesday B Beatrice 15 Wednesday Cecilia 20 Thursday What I need is to find the maximum in name for each group and remove it from the dataframe. The final df would look like: count day group name A Anna 10 Monday B Beatrice 15 Wednesday Does any of you have any idea how to do this? I am running out of ideas... Thanks in advance! EDIT: What if the original