apply

问题 I am looking to apply a function to a data frame and then store the results of that function in a new column in the data frame. Here is a sample of my data frame, tradeData: Login AL Diff a 1 0 a 1 0 a 1 0 a 0 1 a 0 0 a 0 0 a 0 0 a 1 -1 a 1 0 a 0 1 a 1 -1 a 1 0 a 0 1 b 1 0 b 0 1 b 0 0 b 0 0 b 1 -1 c 1 0 c 1 0 c 0 1 c 0 0 c 1 -1 Where the "Diff" column is the column I am trying to add. It just just the difference between the values row(x-1) and row(x) of tradeData, grouped by Login. Here are

问题 Sample data dfData <- data.frame(ID = c(1, 2, 3, 4, 5), DistA = c(10, 8, 15, 22, 15), DistB = c(15, 35, 40, 33, 20), DistC = c(20,40,50,45,30), DistD = c(60,55,55,48,50)) ID DistA DistB DistC DistD 1 1 10 15 20 60 2 2 8 35 40 55 3 3 15 40 50 55 4 4 22 33 45 48 5 5 15 20 30 50 I have some IDs for which there are four columns which measure cumulative distance. I want to create new column that gives the actual distance for each column i.e. subtract the next column from previous column. For e.g.

问题 I have been scouring SO for the best way of applying a function that takes multiple separate Pandas DataFrame columns and outputs multiple new columns in the same said DataFrame. Let's say I have the following: def apply_func_to_df(df): df[['new_A', 'new_B']] = df.apply(lambda x: transform_func(x['A'], x['B'], x['C']), axis=1) def transform_func(value_A, value_B, value_C): # do some processing and transformation and stuff return new_value_A, new_value_B I am trying to apply this function as

问题 I have been scouring SO for the best way of applying a function that takes multiple separate Pandas DataFrame columns and outputs multiple new columns in the same said DataFrame. Let's say I have the following: def apply_func_to_df(df): df[['new_A', 'new_B']] = df.apply(lambda x: transform_func(x['A'], x['B'], x['C']), axis=1) def transform_func(value_A, value_B, value_C): # do some processing and transformation and stuff return new_value_A, new_value_B I am trying to apply this function as

问题 I have been scouring SO for the best way of applying a function that takes multiple separate Pandas DataFrame columns and outputs multiple new columns in the same said DataFrame. Let's say I have the following: def apply_func_to_df(df): df[['new_A', 'new_B']] = df.apply(lambda x: transform_func(x['A'], x['B'], x['C']), axis=1) def transform_func(value_A, value_B, value_C): # do some processing and transformation and stuff return new_value_A, new_value_B I am trying to apply this function as

问题 This is my first post at StackOverflow. I am relatively a newbie in programming and trying to work with the data.table in R, for its reputation in speed. I have a very large data.table, named "Actions", with 5 columns and potentially several million rows. The column names are k1, k2, i, l1 and l2. I have another data.table, with the unique values of Actions in columns k1 and k2, named "States". For every row in Actions, I would like to find the unique index for columns 4 and 5, matching with

问题 Suppose I wanted to get some summary statistics on the dataset mtcars (part of base R version 2.12.1). Below, I group the cars according to the number of engine cylinders they have and take the per-group means of the remaining variables in mtcars . > str(mtcars) 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat:

问题 I have a DataFrame: import pandas as pd import numpy as np x = {'Value': ['Test', 'XXX123', 'XXX456', 'Test']} df = pd.DataFrame(x) I want to replace the values starting with XXX with np.nan using lambda. I have tried many things with replace, apply and map and the best I have been able to do is False, True, True, False. The below works, but I would like to know a better way to do it and I think the apply, replace and a lambda is probably a better way to do it. df.Value.loc[df.Value.str

问题 I have a DataFrame: import pandas as pd import numpy as np x = {'Value': ['Test', 'XXX123', 'XXX456', 'Test']} df = pd.DataFrame(x) I want to replace the values starting with XXX with np.nan using lambda. I have tried many things with replace, apply and map and the best I have been able to do is False, True, True, False. The below works, but I would like to know a better way to do it and I think the apply, replace and a lambda is probably a better way to do it. df.Value.loc[df.Value.str

问题 I have a DataFrame: import pandas as pd import numpy as np x = {'Value': ['Test', 'XXX123', 'XXX456', 'Test']} df = pd.DataFrame(x) I want to replace the values starting with XXX with np.nan using lambda. I have tried many things with replace, apply and map and the best I have been able to do is False, True, True, False. The below works, but I would like to know a better way to do it and I think the apply, replace and a lambda is probably a better way to do it. df.Value.loc[df.Value.str