data-analysis

来源： https://stackoverflow.com/questions/54304913/what-is-major-difference-between-histogram-countplot-and-distplot-in-seaborn-lib

来源： https://stackoverflow.com/questions/46052676/pandas-dataframe-assign-doesnt-update-the-dataframe

来源： https://stackoverflow.com/questions/53100599/applying-the-same-operations-on-multiple-csv-file-in-pandas

来源： https://stackoverflow.com/questions/63006878/using-query-in-a-pandas-dataframe

问题 I'd like to convert my json data to data frame in R. Here is what I've done so far: library("rjson") result <- fromJSON(file ="mypath/data.json") json_data_frame <- as.data.frame(result) However, it comes to an error like this: Error in data.frame(company_id = "12345678", country_name = "China", : arguments imply differing number of rows: 1, 2, 0 I also tried the following code: library("rjson") result <- fromJSON(file ="mypath/data.json") final_data <- do.call(rbind, result) And this error

问题 I'd like to convert my json data to data frame in R. Here is what I've done so far: library("rjson") result <- fromJSON(file ="mypath/data.json") json_data_frame <- as.data.frame(result) However, it comes to an error like this: Error in data.frame(company_id = "12345678", country_name = "China", : arguments imply differing number of rows: 1, 2, 0 I also tried the following code: library("rjson") result <- fromJSON(file ="mypath/data.json") final_data <- do.call(rbind, result) And this error

问题 Following the advice of this post on Analyzing trends in data with pandas, I have used numpy's polyfit on several data I have. However it does not permit me to see when there is a trend and when there isn't. I wonder what am I understanding wrong. First the code is the following import pandas import matplotlib.pyplot as plt import numpy as np file="data.csv" df= pandas.read_csv(file,delimiter=',',header=0) selected=df.loc[(df.index>25)&(df.index<613)] xx=np.arange(25,612) y= selected[selected

问题 I have pandas dataframe, one index(datetime) and three variables(int) date A B C 2017-09-05 25 261 31 2017-09-06 261 1519 151 2017-09-07 188 1545 144 2017-09-08 200 2110 232 2017-09-09 292 2391 325 I can create grouped bar plot with basic pandas plot. df.plot(kind='bar', legend=False) However, I want to display in Seaborn or other libraries to improve my skills. I found very close answer(Pandas: how to draw a bar plot with two categories and four series each?). In its suggested answer, it has

问题 I am new to python data analysis. Following is an example dataset: d2 = {'Index': [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1], 'journey_time':[95.546,132.945,147.538,301.307,42.907,129.008,102.900,112.620,234.334,103.321,82.337,154.817,20.076,85.717,94.362,45.032],'edge':['s_b','c_d','b_d','c_e','d_f','s_a','a_c','d_c','c_e','a_c','d_c','s_a','d_f','s_b','b_d','c_d']} df2=pd.DataFrame(data=d2) I want to create a new data frame where there is one row for each index with new columns. The rules for the

问题 This is the data in single cell of dataframe with 14 columns. Cell is the element of column. There are 45k+ this kind of cells, to do it manually is a hell. one cell data I'd like to do with this cell 3 things: move text part with address, state, zip - to another column; delete the hooks () of cell; separate for 2 columns longitude and latitude. How it's possible to do? 回答1: Here's a simple, working example with 2 data points: text1 = """30881 EKLUTNA LAKE RD CHUGIAK, AK 99567 (61.4478, -149