dataframe

问题 I have a dataframe that I'm gonna use some special latex symbol for its columns and indeces. Something like this : pd.DataFrame({"a":[1,2,3],"b":[4,5,6]}) With a small difference that instead of a and b as the columns I want to have $\diamond$ and $\dagger$ symbols respectively. Any kind of help would be appreciated. 来源： https://stackoverflow.com/questions/53377554/latex-symbols-in-pandas-dataframe

问题 Given a small dataset as follows: value input 0 3 0 1 4 1 2 3 -1 3 2 1 4 3 -1 5 5 0 6 1 0 7 1 1 8 1 1 I have used the following code: df['pct'] = df['value'] / df['value'].sum() But I want to calculate pct by excluding input = -1 , which means if input value is -1 , then the correspondent values will not taken into account to sum up, neither necessary to calculate pct , for rows 2 and 4 at this case. The expected result will like this: value input pct 0 3 0 0.18 1 4 1 0.24 2 3 -1 NaN 3 2 1 0

问题 I'm trying to follow the example on this notebook. As suggested in this github thread: I've upped the ulimit to 9999. I've already converted the csv files to hdf5 My code fails when trying to open a single hdf5 file into a dataframe: df = vaex.open('data/chat_history_00.hdf5') Here's the rest of the code: import re import glob import vaex import numpy as np def tryint(s): try: return int(s) except: return s def alphanum_key(s): """ Turn a string into a list of string and number chunks. "z23a"

问题 Here is a code that I've written, which creates some increments of 3 variables to be used within p-value calculations, where the three variables are loc values or indicators or whatever the numbers mean: i = 0 k = 2 j = 2 result = [] df = pd.DataFrame() while j < data.shape[1]: tstat, data_stat = ttest_ind_from_stats(data.loc[i][k], data.loc[i + 1][k], data.loc[i + 2][k], data.loc[i][j], data.loc[i + 1][j], data.loc[i + 2][j]) result.append([data_stat]) j+=1 if j == 8: j = 2 i = i + 3 if i ==

问题 Let's say i have a pandas dataframe as below: >>> df=pd.DataFrame({'dt':pd.to_datetime(['2018-12-10 16:35:34.246','2018-12-10 16:36:34.243','2018-12-10 16:38:34.216','2018-12-10 16:42:34.123']),'value':[1,2,3,4]}) >>> df dt value 0 2018-12-10 16:35:34.246 1 1 2018-12-10 16:36:34.243 2 2 2018-12-10 16:38:34.216 3 3 2018-12-10 16:42:34.123 4 >>> I would like to group this dataframe by 'dt' column, but i want to group it in a way that it thinks the values that are less than a second different

问题 I use an excel file in which I determine the names of sensor, and a formula allowing me to create a new "synthetic" sensor based on real sensors. I would like to write the formula as string like for example "y1 + y2 + y3" and not "df ['y1'] + df ['y2'] + df ['y3]" but I don't see which method to use? Excel file example: My script must therefore create a new sensor for each line of this excel file. This new sensor will then be uploaded to my database. The number of sensors to calculate the new

问题 I have two dataframes in R named house and candidates. house House Region Military_Strength 1 Stark The North 20000 2 Targaryen Slaver's Bay 110000 3 Lannister The Westerlands 60000 4 Baratheon The Stormlands 40000 5 Tyrell The Reach 30000 candidates House Name Region 1 Lannister Jamie Lannister Westros 2 Stark Robb Stark North 3 Stark Arya Stark Westros 4 Lannister Cersi Lannister Westros 5 Targaryen Daenerys Targaryen Mereene 6 Baratheon Robert Baratheon Westros 7 Mormont Jorah Mormont

问题 Let's say I have a dataframe: import numpy as np import pandas as pd df = pd.DataFrame(np.random.normal(0,1,[100,50])) that looks like: 0 1 2 3 4 5 6 \ 0 -0.141305 2.158252 1.006520 -1.004185 -0.213160 0.648904 -0.089369 1 -1.373167 -1.100959 1.007023 0.699591 -1.667834 1.422182 0.940912 2 -0.212014 1.967436 0.401133 -0.996298 -1.696490 -0.857453 -0.686584 3 -0.351902 0.413816 -0.494869 0.448740 0.146897 -0.798095 -0.546489 4 0.416376 -0.689577 -0.967050 -1.667480 1.223966 -1.382113 -0.812368

问题 Let's say I have a dataframe: import numpy as np import pandas as pd df = pd.DataFrame(np.random.normal(0,1,[100,50])) that looks like: 0 1 2 3 4 5 6 \ 0 -0.141305 2.158252 1.006520 -1.004185 -0.213160 0.648904 -0.089369 1 -1.373167 -1.100959 1.007023 0.699591 -1.667834 1.422182 0.940912 2 -0.212014 1.967436 0.401133 -0.996298 -1.696490 -0.857453 -0.686584 3 -0.351902 0.413816 -0.494869 0.448740 0.146897 -0.798095 -0.546489 4 0.416376 -0.689577 -0.967050 -1.667480 1.223966 -1.382113 -0.812368

问题 Let's say I have a dataframe: import numpy as np import pandas as pd df = pd.DataFrame(np.random.normal(0,1,[100,50])) that looks like: 0 1 2 3 4 5 6 \ 0 -0.141305 2.158252 1.006520 -1.004185 -0.213160 0.648904 -0.089369 1 -1.373167 -1.100959 1.007023 0.699591 -1.667834 1.422182 0.940912 2 -0.212014 1.967436 0.401133 -0.996298 -1.696490 -0.857453 -0.686584 3 -0.351902 0.413816 -0.494869 0.448740 0.146897 -0.798095 -0.546489 4 0.416376 -0.689577 -0.967050 -1.667480 1.223966 -1.382113 -0.812368