concatenation

问题 I am trying to write a code that generate variable using a-z only and maximum of 4 char , that means a total of 26*26*26*26 combinations.So here is what i am doing #include<stdio.h> static char vcd_xyz[4]; static char vcd_xyz1[2]; int main() { int i,j; for(i=0;i<26;i++) { vcd_xyz[1] = 'a'+i; printf("%d generated variable is initial is = %c \n",i,vcd_xyz[1]); for(j=0;j<26;j++) { vcd_xyz[2] = 'a'+j; printf("%d generated variable is = %c \n",j,vcd_xyz[2]); //strcat(vcd_xyz[1],vcd_xyz[2]); } }

问题 I have a number of dataframes (100) in a list as: frameList = [df1,df2,..,df100] Each dataframe has the two columns DateTime , Temperature . I want to intersect all the dataframes on the common DateTime column and get all their Temperature columns combined/merged into one big dataframe: Temperature from df1, Temperature from df2, Temperature from df3, .., Temperature from df100. (pandas merge doesn't work as I'd have to compute multiple (99) pairwise intersections). 回答1: Use pd.concat , which

问题 I have a number of dataframes (100) in a list as: frameList = [df1,df2,..,df100] Each dataframe has the two columns DateTime , Temperature . I want to intersect all the dataframes on the common DateTime column and get all their Temperature columns combined/merged into one big dataframe: Temperature from df1, Temperature from df2, Temperature from df3, .., Temperature from df100. (pandas merge doesn't work as I'd have to compute multiple (99) pairwise intersections). 回答1: Use pd.concat , which

问题 I have a number of dataframes (100) in a list as: frameList = [df1,df2,..,df100] Each dataframe has the two columns DateTime , Temperature . I want to intersect all the dataframes on the common DateTime column and get all their Temperature columns combined/merged into one big dataframe: Temperature from df1, Temperature from df2, Temperature from df3, .., Temperature from df100. (pandas merge doesn't work as I'd have to compute multiple (99) pairwise intersections). 回答1: Use pd.concat , which

问题 I have the following series: s = pd.Series([['a', 'b'], ['c', 'd'], ['f', 'g']]) >>> s 0 [a, b] 1 [c, d] 2 [f, g] dtype: object what is the easiest - preferably vectorized - way to concatenate all lists in the series, so that I get: l = ['a', 'b', 'c', 'd', 'f', 'g'] Thanks! 回答1: A nested list comprehension should be much faster. >>> [element for list_ in s for element in list_] ['a', 'b', 'c', 'd', 'f', 'g'] >>> %timeit -n 100000 [element for list_ in s for element in list_] 100000 loops,

问题 I'm trying to read multiple CSV files that have the same structure(column names)and located in several folders, My main purpose is to concatenate these files into one panda data frame. please find attached below files location distribution of folders, thus each folder contains 5 CSV files. Is there any predefined function or smth that can help ?? 回答1: Using the os.walk() and pd.concat(): import os import pandas as pd outdir = [YOUR_INITIAL_PATH] df_final = pd.DataFrame(columns=['column1',

问题 I'm trying to read multiple CSV files that have the same structure(column names)and located in several folders, My main purpose is to concatenate these files into one panda data frame. please find attached below files location distribution of folders, thus each folder contains 5 CSV files. Is there any predefined function or smth that can help ?? 回答1: Using the os.walk() and pd.concat(): import os import pandas as pd outdir = [YOUR_INITIAL_PATH] df_final = pd.DataFrame(columns=['column1',

问题 I have a dataframe that looks roughly like: 2020-01-01 2020-01-02 2020-01-03 2020-01-05 00:00:00 11 47 54 10 01:00:00 12 49 46 22 ... 23:00:00 15 34 22 40 Expected output... 2020-01-01 00:00:00 11 2020-01-01 01:00:00 12 ... 2020-01-01 23:00:00 12 2020-01-02 00:00:00 47 2020-01-02 01:00:00 49 ... 2020-01-01 23:00:00 34 ... 回答1: Use DataFrame.melt with convert index to column, then convert columns to datetimes and timedeltas and join together with DataFrame.pop for extract columns: df = df

问题 I have a dataframe that looks roughly like: 2020-01-01 2020-01-02 2020-01-03 2020-01-05 00:00:00 11 47 54 10 01:00:00 12 49 46 22 ... 23:00:00 15 34 22 40 Expected output... 2020-01-01 00:00:00 11 2020-01-01 01:00:00 12 ... 2020-01-01 23:00:00 12 2020-01-02 00:00:00 47 2020-01-02 01:00:00 49 ... 2020-01-01 23:00:00 34 ... 回答1: Use DataFrame.melt with convert index to column, then convert columns to datetimes and timedeltas and join together with DataFrame.pop for extract columns: df = df

问题 I am testing out concating mp4 files together and have come across one way to do it that is working but is creating huge file sizes. my original videos are mp4 and I started out trying to convert them to mpeg after following other guides but found that mpeg would not convert as my file is 1fps and mpeg does not support that. media@v2:~/mp4_test$ avconv -i video1.mp4 video1.mpeg avconv version 9.16-6:9.16-0ubuntu0.14.04.1, Copyright (c) 2000-2014 the Libav developers built on Aug 10 2014 18:16