dataframe

问题 I have many columns in a data frame and I have to find the difference of time in two column named as in_time and out_time and put it in the new column in the same data frame. The format of time is like this 2015-09-25T01:45:34.372Z . I am using Pandas DataFrame. I want to do like this: df.days = df.out_time - df.in_time I have many columns and I have to increase 1 more column in it named days and put the differences there. 回答1: You need to convert the strings to datetime dtype, you can then

问题 I have many columns in a data frame and I have to find the difference of time in two column named as in_time and out_time and put it in the new column in the same data frame. The format of time is like this 2015-09-25T01:45:34.372Z . I am using Pandas DataFrame. I want to do like this: df.days = df.out_time - df.in_time I have many columns and I have to increase 1 more column in it named days and put the differences there. 回答1: You need to convert the strings to datetime dtype, you can then

问题 I have a data frame df1: df1 = pd.DataFrame([[40, 23, 22, 31], [12, 3, 11,23], [42, 16, 32, 34], [42, 13, 26, 34]], columns=['A', 'B', 'C', 'D']) I have another data frame df2: df2 = pd.DataFrame([["B","<20"],["A",">30"],["C","<40"],["D","<15"]], columns=["Column","Condition"]) Question: Select the data frame df1 based on any of the conditions present in df2. How to do it? Please help. Expected Output example: For B Condition: B_df = pd.DataFrame([3,16,13],columns=["B"]) For C Condition: C_df

问题 I have a data frame df1: df1 = pd.DataFrame([[40, 23, 22, 31], [12, 3, 11,23], [42, 16, 32, 34], [42, 13, 26, 34]], columns=['A', 'B', 'C', 'D']) I have another data frame df2: df2 = pd.DataFrame([["B","<20"],["A",">30"],["C","<40"],["D","<15"]], columns=["Column","Condition"]) Question: Select the data frame df1 based on any of the conditions present in df2. How to do it? Please help. Expected Output example: For B Condition: B_df = pd.DataFrame([3,16,13],columns=["B"]) For C Condition: C_df

问题 So I have the following code which reads in 5 columns, date ohlc. It then creates a column 'dow' to hold day of week. So far so good: import numpy as np import pandas as pd df = pd.read_csv('/content/drive/MyDrive/Forex/EURUSD-2018_12_18-2020_11_01.csv',parse_dates=True,names = ['date','1','2','3','4',]) df['date'] = pd.to_datetime(df['date']) df.index = df['date'] df['dow'] = df['date'].dt.dayofweek #df['downum'] = df.apply(lambda x: downu(x['date'])) df Producing the following output: date

问题 Question: how can I specifically add .0 to all df$x -values not containing decimals? Take the following a dataframe df <- data.frame(x=c("2.8","9","0.5","1.2","4","12")) > head(df) x 1 2.8 2 9 3 0.5 4 1.2 5 4 6 12 The desired result looks like this > head(df) x 1 2.8 2 9.0 3 0.5 4 1.2 5 4.0 6 12.0 EDIT I want to automatically add .0 to all integers in ee$x , which is included below. I tried ee$x <- as.numeric(as.character(ee$x)) but that didn't seem to work. Could anything be wring with my

问题 Question: how can I specifically add .0 to all df$x -values not containing decimals? Take the following a dataframe df <- data.frame(x=c("2.8","9","0.5","1.2","4","12")) > head(df) x 1 2.8 2 9 3 0.5 4 1.2 5 4 6 12 The desired result looks like this > head(df) x 1 2.8 2 9.0 3 0.5 4 1.2 5 4.0 6 12.0 EDIT I want to automatically add .0 to all integers in ee$x , which is included below. I tried ee$x <- as.numeric(as.character(ee$x)) but that didn't seem to work. Could anything be wring with my

问题 I've been asked to create a data frame in R using information copied from a website; the data is not contained in a file. The full data list is at: https://www.npr.org/2012/12/07/166400760/hollywood-heights-the-ups-downs-and-in-betweens Here is a portion of the data: Leading Men (Average American male: 5 feet 9.5 inches) Dolph Lundgren — 6 feet 5 inches John Cleese — 6 feet 5 inches Michael Clarke Duncan — 6 feet 5 inches Vince Vaughn — 6 feet 5 inches Clint Eastwood — 6 feet 4 inches Jimmy

问题 Recently, I asked the following question - Unwanted white spaces resulting into distorted column and the answer by @sharathnatraj was satisfactory and worked like a charm. Answer was: import re with open('trial1.txt', 'r') as f: lines = f.readlines() l = [re.sub(r"([a-z]{5,})\s([a-z]{5,})", r"\1\2", line) for line in lines] df = pd.read_csv(io.StringIO('\n'.join(l)), delim_whitespace=True) Sample data set: 1 CAgF3O3S silver trifluoromethanesulfonate 2923-28-6 256.937 629.15 1 --- --- --- ---

问题 Recently, I asked the following question - Unwanted white spaces resulting into distorted column and the answer by @sharathnatraj was satisfactory and worked like a charm. Answer was: import re with open('trial1.txt', 'r') as f: lines = f.readlines() l = [re.sub(r"([a-z]{5,})\s([a-z]{5,})", r"\1\2", line) for line in lines] df = pd.read_csv(io.StringIO('\n'.join(l)), delim_whitespace=True) Sample data set: 1 CAgF3O3S silver trifluoromethanesulfonate 2923-28-6 256.937 629.15 1 --- --- --- ---