I have the following DF: created_at id Sun Jan 16 11:55:32 +0000 2022 1,48268E+18 Sun Jan 16 11:09:47 +0000 2022 1,48267E+18 Sun Jan 16 10:48:26 +0000 2022 1,48267E+18 Sun Jan 16 09:04:36 +0000 2022 1,48264E+18 Sun Jan 16 05:07:22 +0000 2022 1,48258E+18 Sat Jan 15 20:16:53 +0000 2022 1,48245E+18 How to convert created_at column to ..
Category : dataframe
I am trying to take all columns with a ‘%’ and removing the ‘%,’ turning the string into a float, and diving by 100 and turning it into a decimal. I created a list of all the columns that I want to do that to with: percentages = (df.filter(like=’%’).columns) perclist = percentages.tolist() and then I ..
index key M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 Average Count_G10 Count_L10 0 a 12 0 159 0 20 49 0 131 157 153 68.1 4 3 1 b 0 68 195 189 0 79 12 179 21 62 80.5 3 4 2 c 0 139 0 188 12 0 31 87 ..
I trying to iterate through pandas table and add PDF page for each row using FPDF library. The script only adds the very last record in the table and generates one page. I’m trying to figure out how to add a page for each record import pandas as pd from fpdf import FPDF filename = ..
I am experimenting with polars and would like to understand why using polars is slower than using pandas on a particular example: import pandas as pd import polars as pl n=10_000_000 df1 = pd.DataFrame(range(n), columns=[‘a’]) df2 = pd.DataFrame(range(n), columns=[‘b’]) df1p = pl.from_pandas(df1.reset_index()) df2p = pl.from_pandas(df2.reset_index()) # takes ~60 ms df1.join(df2) # takes ~950 ms df1p.join(df2p, ..
I have a pandas dataframe like this: A B C D E 0 aaa 5.56000000 0.01000000 1.00000000 0.00001000 1 bbb 0.49000000 0.00100000 0.01000000 0.00010000 2 ccc 1.70000000 0.10000000 0.10000000 0.00100000 3 ddd 5.35000000 0.00001000 0.00001000 0.01000000 4 eee 6.00000000 16.00000000 0.00000100 1.00000000 I need remove trailing zeros and at the same time, round values after ..
Hello everyone, I would like to redesign the date column. It should be date format instead of 201501 but I couldn’t handle it. Could you please help me? Thanks in advance. Source: Python..
i face a problem with pivot in pandas , the total_profit and numberofgoodsold columns are located above company row. i need the company row to be at the top. in each company the total_profit and the goodsold columns should came under. this is my code: data = {‘company’: [‘AMC’, ‘ER’,’CRR’ , ‘TYU’], ‘Reg-ID’: [‘1222′,’2334′,’3444’, ‘4566’], ..
Input is 2 pandas Dataframe df1 & df2 df1 Document No Amount 6 8138000628REV 0.00 9 8138000602REV 0.00 24 8138000607REV 310.00 11 8138000605REV 0.00 14 813800602REV 0.00 45 8138000525AREV 0.00 84 8138000861REV 200000.00 87 8138000748REV -80770.82 df2 Document No Amount 2 8138000628 0.00 5 8138000602 0.00 12 8138000605 0.00 16 813800602 0.00 42 8138000525A 0.00 ..
I want to add an array to an existing pandas dataframe as row. Below is my code : import pandas as pd import numpy as np data = [[‘tom’, 10]] df = pd.DataFrame(data, columns = [‘Name’, ‘Age’]) print(df) Y = [10, 100] df.loc[0] = list(Y) print(df) Basically I want to add Y to df as ..
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