pandas

问题 I am building a simple python dashboard using dash and plotly. I am also new to python (as is probably evident!) and I'm happy for any/all corrections. I would like to plot a time series of data from a pre-determined CSV file. I have added a dropdown selection box with which I would like to allow multiple different columns to be plotted. Sample data: "TOA5","HE605_RV50_GAF","CR6","7225","CR6.Std.07","CPU:BiSP5_GAF_v2d.CR6","51755","SensorStats" "TIMESTAMP","RECORD","BattV_Min","BattV_Avg",

问题 When i use pandas to convert csv files to hdf5 files the resulting file is extremely large. For example a test csv file (23 columns, 1.3 million rows) of 170Mb results in an hdf5 file of 2Gb. However if pandas is bypassed and the hdf5 file is directly written (using pytables) it is only 20Mb. In the following code (that is used to do the conversion in pandas) the values of the object columns in the dataframe are explicitly converted to string objects (to prevent pickling): # Open the csv file

问题 I am new to python. Just following the tutorial: https://www.hackerearth.com/practice/machine-learning/machine-learning-projects/python-project/tutorial/ This is the dataframe miss: miss = train.isnull().sum()/len(train) miss = miss[miss>0] miss.sort_values(inplace = True) miss Electrical 0.000685 MasVnrType 0.005479 MasVnrArea 0.005479 BsmtQual 0.025342 BsmtCond 0.025342 BsmtFinType1 0.025342 BsmtExposure 0.026027 BsmtFinType2 0.026027 GarageCond 0.055479 GarageQual 0.055479 GarageFinish 0

问题 I am new to python. Just following the tutorial: https://www.hackerearth.com/practice/machine-learning/machine-learning-projects/python-project/tutorial/ This is the dataframe miss: miss = train.isnull().sum()/len(train) miss = miss[miss>0] miss.sort_values(inplace = True) miss Electrical 0.000685 MasVnrType 0.005479 MasVnrArea 0.005479 BsmtQual 0.025342 BsmtCond 0.025342 BsmtFinType1 0.025342 BsmtExposure 0.026027 BsmtFinType2 0.026027 GarageCond 0.055479 GarageQual 0.055479 GarageFinish 0

问题 I am trying to read an excel file that has date columns with the below code src1_df = pd.read_excel("src_file1.xlsx", keep_default_na = False) Even though I have specified, keep_default_na = False, I see that the data frame has 'NaT' value(s) for corresponding blank cells in Excel date columns. Please suggest, how to get a blank string instead of 'NaT' while parsing Excel files. I am using Python 3.x and Pandas 0.23.4 回答1: src1_df = pd.read_excel("src_file1.xlsx", na_filter=False) Then you

问题 I'm trying to parse from a .csv file: planets = pd.read_csv("planets.csv", sep=',') But I always end up with this error: ParserError: Error tokenizing data. C error: Expected 1 fields in line 13, saw 9 This is how the first few lines of my csv file look like: # This file was produced by the test # Tue Apr 3 06:03:27 2018 # # COLUMN pl_hostname: Host Name # COLUMN pl_discmethod: Discovery Method # COLUMN pl_pnum: Number of Planets in System # COLUMN pl_orbper: Orbital Period [days] # COLUMN pl

问题 I have a df looks like, A B C D 2017-10-01 2017-10-11 M 2017-10 2017-10-02 2017-10-03 M 2017-10 2017-11-01 2017-11-04 B 2017-11 2017-11-08 2017-11-09 B 2017-11 2018-01-01 2018-01-03 A 2018-01 the dtype of A and B are datetime64 , C and D are of strings ; I like to groupby C and D and get the differences between B and A , df.groupby(['C', 'D']).apply(lambda row: row['B'] - row['A']) but I don't know how to sum such differences in each group and assign the values to a new column say E ,

问题 I'm trying to parse from a .csv file: planets = pd.read_csv("planets.csv", sep=',') But I always end up with this error: ParserError: Error tokenizing data. C error: Expected 1 fields in line 13, saw 9 This is how the first few lines of my csv file look like: # This file was produced by the test # Tue Apr 3 06:03:27 2018 # # COLUMN pl_hostname: Host Name # COLUMN pl_discmethod: Discovery Method # COLUMN pl_pnum: Number of Planets in System # COLUMN pl_orbper: Orbital Period [days] # COLUMN pl

问题 I'm trying to get the advanced stats of players onto an excel sheet but the table it's scraping is the first one instead of the advanced stats table. ValueError: Length of passed values is 23, index implies 21 If i try to use the id instead, i get an another error about tbody. Also, I get an error about lname=name.split(" ")[1] IndexError: list index out of range. I think that has to do with 'Nene' in the list. Is there a way to fix that? import requests from bs4 import BeautifulSoup

问题 I have a df looks like, A B C D 2017-10-01 2017-10-11 M 2017-10 2017-10-02 2017-10-03 M 2017-10 2017-11-01 2017-11-04 B 2017-11 2017-11-08 2017-11-09 B 2017-11 2018-01-01 2018-01-03 A 2018-01 the dtype of A and B are datetime64 , C and D are of strings ; I like to groupby C and D and get the differences between B and A , df.groupby(['C', 'D']).apply(lambda row: row['B'] - row['A']) but I don't know how to sum such differences in each group and assign the values to a new column say E ,