pandas

问题 Function f(x,y) that takes two Pandas Series and returns a floating point number. I would like to apply f to each pair of columns in a DataFrame D and construct another DataFrame E of the returned values, so that f(D[i],D[j]) is the value of the i th row and j th column. The straightforward solution is to run a nested loop over all pairs of columns: E = pd.DataFrame([[f(D[i], D[j]) for i in D] for j in D], columns=D.columns, index=D.columns) But is there a more elegant solution that perhaps

问题 Function f(x,y) that takes two Pandas Series and returns a floating point number. I would like to apply f to each pair of columns in a DataFrame D and construct another DataFrame E of the returned values, so that f(D[i],D[j]) is the value of the i th row and j th column. The straightforward solution is to run a nested loop over all pairs of columns: E = pd.DataFrame([[f(D[i], D[j]) for i in D] for j in D], columns=D.columns, index=D.columns) But is there a more elegant solution that perhaps

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 This question already has answers here : Pandas max value index (3 answers) Closed 2 years ago . I have the following dataframe: data = {'Algorithm': ['KNN', 'Decision Tree', 'SVM', 'Logistic Regression'], 'Jaccard': [0.75,0.65,0.67,0.70], 'F1-score': [0.69,0.78, 0.75, 0.77], 'LogLoss': ['NA', 'NA', 'NA', 5.23]} report= pd.DataFrame(data = data) report = report[['Algorithm', 'Jaccard', 'F1-score', 'LogLoss']] report.set_index(report['Algorithm'], inplace = True) report.drop(columns = [

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 I have a python script that reads data from the TWS API (Interactive Brokers) and want to dump the data in a csv file. Right now it just overwrites the data and prints the last line along with a bunch of other values I don't want. It prints out the values fine with print(df). Code: from ibapi.client import EClient from ibapi.wrapper import EWrapper from ibapi.common import * from ibapi.contract import * from threading import Timer from ibapi.ticktype import * import pandas as pd import numpy

问题 This question already has answers here : Pandas: assign an index to each group identified by groupby (6 answers) Closed 2 years ago . I have a DataFrame with columns 'Id' which is unique, and 'A', 'B', 'C' , etc... There are different rows where all values 'A', 'B', 'C' are the same. I'd like to give them a group name (a running index from 1). For example: df = pd.DataFrame({"A": [1, 1, 1, 2], "B": [3, 4, 4, 4], "C": [5, 5, 5, 5]}) df Out[127]: A B C 0 1 3 5 1 1 4 5 2 1 4 5 3 2 4 5 Will

问题 This question already has answers here : Pandas: assign an index to each group identified by groupby (6 answers) Closed 2 years ago . I have a DataFrame with columns 'Id' which is unique, and 'A', 'B', 'C' , etc... There are different rows where all values 'A', 'B', 'C' are the same. I'd like to give them a group name (a running index from 1). For example: df = pd.DataFrame({"A": [1, 1, 1, 2], "B": [3, 4, 4, 4], "C": [5, 5, 5, 5]}) df Out[127]: A B C 0 1 3 5 1 1 4 5 2 1 4 5 3 2 4 5 Will