multiple-columns

问题 I have a huge DataFrame , where some columns have the same names. When I try to pick a column that exists twice, (eg del df['col name'] or df2=df['col name'] ) I get an error. What can I do? 回答1: You can adress columns by index: >>> df = pd.DataFrame([[1,2],[3,4],[5,6]], columns=['a','a']) >>> df a a 0 1 2 1 3 4 2 5 6 >>> df.iloc[:,0] 0 1 1 3 2 5 Or you can rename columns, like >>> df.columns = ['a','b'] >>> df a b 0 1 2 1 3 4 2 5 6 回答2: Another solution: def remove_dup_columns(frame): keep

问题 I have two dataframes : df1 and df2 df1 : TIMESTAMP eq1 eq2 eq3 2016-05-10 13:20:00 40 30 10 2016-05-10 13:40:00 40 10 20 df2 : TIMESTAMP eq1 eq2 eq3 2016-05-10 13:20:00 10 20 30 2016-05-10 13:40:00 10 20 20 I would like to divide df1 by df2 : each column of df1 by all column of df2 to get this result df3 : TIMESTAMP eq1 eq2 eq3 2016-05-10 13:20:00 40/(10+10) 30/(20+20) 10/(30+20) 2016-05-10 13:40:00 40/(10+10) 10/(20+20) 20/(30+20) Any idea please? 回答1: You can use div, but before set_index

问题 Need help expanding all columns in a spreadsheet simultaneously using Power Query. I have transposed the spreadsheet from this: to this: Each table is a long column of values (9,000+ rows). I would like each column to be a separate ID. Expanding columns manually would be a tedious job and our team is adding data from new study participants (IDs) regularly, so I need help creating a code that can expand all columns simultaneously without having to indicate the column names (IDs) in the code.

问题 I have two separate files, each containing a different number of columns which I want to merge based on data in multiple columns. file1 VMNF01000015.1 1769465 1769675 . . - Focub_II5_mimp_1 VMNF01000014.1 3225875 3226081 . . + Focub_II5_mimp_1 VMNF01000014.1 3226046 3226081 . . - Focub_II5_mimp_1 VMNF01000014.1 3585246 3585281 . . - Focub_II5_mimp_1 VMNF01000014.1 3692468 3692503 . . - Focub_II5_mimp_1 VMNF01000014.1 3715380 3715415 . . + Focub_II5_mimp_1 VMNF01000014.1 2872478 2872511 . . -

问题 I am trying to write sub-queries so that I search all tables for a column named id and since there are multiple tables with id column, I want to add the condition, so that id = 3119093 . My attempt was: Select * from information_schema.tables where id = '3119093' and id IN ( Select table_name from information_schema.columns where column_name = 'id' ); This didn't work so I tried: Select * from information_schema.tables where table_name IN ( Select table_name from information_schema.columns

问题 I'm trying to figure out how to encrypt a columnar transposition cipher in Python given a plaintext uppercase string and a number key of any length. For example, if the key is 3124 and the string is 'IHAVETWOCATS', it would organize the string like so: 3124 IHAV ETWO CATS and then return the characters in column 1 first, then column 2, etc, until finally returning the encrypted string 'HTAAWTIECVOS' . So far I know that I'll need to use an accumulator, and I've been toying with the idea of

问题 I am an R user who is currently learning Python and I am trying to replicate a method of selecting columns used in R into Python. In R, I could select multiple columns like so: df[,c(2,4:10)] In Python, I know how iloc works, but I couldn't split between a single column number and a consecutive set of them. This wouldn't work df.iloc[:,[1,3:10]] So, I'll have to drop the second column like so: df.iloc[:,1:10].drop(df.iloc[:,1:10].columns[1] , axis=1) Is there a more efficient way of

问题 I am an R user who is currently learning Python and I am trying to replicate a method of selecting columns used in R into Python. In R, I could select multiple columns like so: df[,c(2,4:10)] In Python, I know how iloc works, but I couldn't split between a single column number and a consecutive set of them. This wouldn't work df.iloc[:,[1,3:10]] So, I'll have to drop the second column like so: df.iloc[:,1:10].drop(df.iloc[:,1:10].columns[1] , axis=1) Is there a more efficient way of

问题 I know that I can get the number of rows having a particular value in a particular column by making use of the COUNT(*) function, but I want to find the number of COLUMNS in a particular row that have a particular value. Any suggestions on how to do this? I would have posted an example of what I've tried up till now, but I'm completely lost on this one... Edit 1 - Here's some sample data and the expected result: Table - trackbill | u1 | u1paid | u2 | u2paid | u3 | u3paid | u4 | u4paid | u5 |

问题 Input: a;3;c;1 a;4;b;2 a;5;c;1 Output: a;3;c;1 a;5;c;1 Hence, all lines which have duplicates of columns 1,3 and 4 should be printed. 回答1: If a 2-pass approach is OK: $ awk -F';' '{key=$1 FS $3 FS $4} NR==FNR{cnt[key]++;next} cnt[key]>1' file file a;3;c;1 a;5;c;1 otherwise: $ awk -F';' ' { key=$1 FS $3 FS $4; a[key,++cnt[key]]=$0 } END { for (key in cnt) if (cnt[key] > 1) for (i=1; i<=cnt[key]; i++) print a[key,i] } ' file a;3;c;1 a;5;c;1 The output order of keys in that second script will be