I wrote a part here, that is to say, first convert the original df into a dictionary, and then create a new dictionary
and then operate on these two dictionaries, but there seems to be a problem with the result of a large amount of data
I wrote a part here, that is to say, first convert the original df into a dictionary, and then create a new dictionary
and then operate on these two dictionaries, but there seems to be a problem with the result of a large amount of data
df.columns = ['d', 't', 'n']
res = df.merge(pd.DataFrame(list(range(24)),columns=['t']),on='t',how='right').pivot(index='d', columns='t', values='n').dropna(how='all').fillna(0)currently I have a piece of code that spends most of its time on the above two sentences of data filtering in dataframe. temp_df = df [df [ "data_date "] .isin (date_list)] temp = temp_df [rule [2]] [temp_df [ "data_date "] = = d] at present, it tak...
how to deal with decoding errors when reading files? ...
import pandas as pd word = pd.read_table ( test.txt , encoding = utf-8 , names = [ query ]) what does the query in the names here mean? header: int, list of ints, default infer Row number (s) to use as the column names, and the s...
I have a txt file encoded in unicode, : 1.with open( STK_MKT_ValuationMetrics.txt , rb ) as f:: utf-8 codec can t decode byte 0xff in position 0: invalid start byte2.with open( STK_MKT_ValuationMetrics.txt , rb ,encoding= utf-8 ) as ...
the value in the figure is "pass " After pandas reads the csv file, I want to delete the data line whose value is a string in the kscj column. What should I do? ...
csv`import numpy as npimport pandas as pdf=open( G:XueYegrades.csv , rb )df=pd.read_csv(f,low_memory=False,usecols=[0,1,3,4,5,7,8,15,16])group=df.groupby([ xh , xm ],sort=False)[ xf ]print(group.sum())` as a result, he counted the student n...
to export a batch of Json data from Mongodb, you need to transfer to Mysql, but the exported Json format cannot be directly written to mysql, so you want to convert the data to Pandas s dataframe, and then write to sql: through dataframe . import panda...
df = pd.DataFrame([[4, 9],[4, 2], [4, 5], [5, 4]], columns=[ A , B ]) df.groupby([ A ]).apply(lambda x : print(x, n )) df is: A B 0 4 9 1 4 2 2 4 5 3 5 4 the output after using apply is as follows: A B 0 4 9 ...
when using pandas for file writing, if the original sheet already has data, the newly written data is overwritten on the original data without deletion. For example, there are 4 rows of data originally, and I want to delete one row. After read is datafr...
def hour_exceed (df): i=df.values if i is np.nan: return np.nan elif i>200: return 1 elif i<200: return 0 < H1 > dataframe < H1 > df15.head () Out [21]: time 1036A 1037A 1040A 1041A 1051A 1053A 1054A 0 2...
Baidu interview questions, roughly means that there is a file, the file is very large can not be read at one time (may not be loaded into memory), the file is stored in the IP address, how to quickly find the duplicate IP address? Ask for advice. The ...
Ladies and Gentlemen, I would like to ask you a Pandas grouping question, which I feel is more complicated. df = pd.DataFrame({"Date":pd.date_range(start= 2018-08-17 08:10:30 ,periods=15,freq= s ,normalize=True),"Category":list(...
1. When I am converting the real data of two dataframe tables into numpy data for column content comparison, I encounter the following situations: input: individual elements of different arrays d12.values [0] [0], d12.values [1] [0], d11.values [...
pandas read_sql is too slow 10W rows of data take about 10 seconds. Is there an optimization plan ? ...
use pandas s read_csv to read a dataset import pandas as pd dfoff = pd.read_csv (xxx.csv , keep_default_na=False) pd.set_option ( display.max_columns , None) print (dfoff.head (3)) found that he has 6 columns of data User_id print(dfof...
know the start time and end time of each sample, as shown in the following figure: hu is the unique value of the sample. It is known that time1 and time2 are the start time and end time of the behavior, respectively. Now, we want to count the number...
df = pd.DataFrame({ key1 : [ a , a , a , b , b ], key2 : [ c , d , c , c , d ], data : [1,10,2,3,30]}) >>> df key1 key2 data 0 a c 1 1 a d 10 2 a c 2 3 b c 3 4 ...
encountered a problem and simplified it. has a dataframe df = pd.DataFrame([[ a , 1, c ], [ a , 3, a ], [ a , 2, b ], [ c , 3, a ], [ c , 2, b ], [ c , 1, c ], [ b , 2, b ], [ ...
two columns of existing data, as shown in the following figure: then I want to delete the data in column a that matches a regular expression (such as the beginning of 0002). How should I write it? add: the above is just an example, because it doe...
the reason has been found. The order of variables from append to the data box will be adjusted automatically when the column tag of DataFrame is not set beforehand. df = pd.DataFrame() series=pd.Series([3,4,1,6],index=[ b , a , d , c ]) df=df.a...