pandas

问题 How to append 2 different dataframes with different column names a = pd.DataFrame({ "id": [0,1,2,3], "countryid": [22,36,21,64], "famousfruit": ["banana", "apple", "mango", "orange"], "famousanimal": ["monkey", "elephant", "monkey", "horse"], "waterlvl": [23, 43, 41, 87] }).set_index("id") >> a b = pd.DataFrame({ "id": [0,1,2,3], "cid": [25,27,98,67], "FAM_FRUIT": ["grapes", "pineapple", "avacado", "orange"], "FAM_ANI": ["giraffe", "dog", "cat", "horse"], }).set_index("id") >>b How to append

问题 I am just getting started in Python and Machine Learning and have encountered an issue which I haven't been able to fix myself or with any other online resource. I am trying to scale a column in a pandas dataframe using a lambda function in the following way: X['col1'] = X['col1'].apply(lambda x: (x - x.min()) / (x.max() - x.min())) and get the following error message: 'float' object has no attribute 'min' I have tried to convert the data type into integer and the following error is returned:

问题 I am just getting started in Python and Machine Learning and have encountered an issue which I haven't been able to fix myself or with any other online resource. I am trying to scale a column in a pandas dataframe using a lambda function in the following way: X['col1'] = X['col1'].apply(lambda x: (x - x.min()) / (x.max() - x.min())) and get the following error message: 'float' object has no attribute 'min' I have tried to convert the data type into integer and the following error is returned:

问题 I'm testing the example code provided in the documentation of pandas_udf (https://spark.apache.org/docs/2.3.1/api/python/pyspark.sql.html#pyspark.sql.functions.pandas_udf), using Pyspark 2.3.1 on my local machine: from pyspark.sql import SparkSession from pyspark.sql.functions import pandas_udf, PandasUDFType df = spark.createDataFrame( [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id", "v")) @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP) def normalize(pdf): v = pdf.v

问题 Let's say I have a pandas DataFrame (loaded from a csv file) with this structure (the number of var and err columns is not fixed, and it varies from file to file): var_0; var_1; var_2; 32; 9; 41; 47; 22; 41; 15; 12; 32; 3; 4; 4; 10; 9; 41; 43; 21; 45; 32; 14; 32; 51; 20; 40; Let's discard the err_ds_j and the err_mean columns for the sake of this question. I have to perform an automatic comparison of the values of each row, with the values of the other rows; as an example: I have to compare

问题 I'm trying to solve a problem for a course in Python and found someone has implemented solutions for the same problem in github. I'm just trying to understand the solution given in github. I have a pandas dataframe called Top15 with 15 countries and one of the columns in the dataframe is 'HighRenew'. This column stores the % of renewable energy used in each country. My task is to convert the column values in 'HighRenew' column into boolean datatype. If the value for a particular country is

问题 I want to do what they've done in the answer here: Calculating the number of specific consecutive equal values in a vectorized way in pandas , but using a grouped dataframe instead of a series. So given a dataframe with several columns A B C ------------ x x 0 x x 5 x x 2 x x 0 x x 0 x x 3 x x 0 y x 1 y x 10 y x 0 y x 5 y x 0 y x 0 I want to groupby columns A and B, then count the number of consecutive zeros in C. After that I'd like to return counts of the number of times each length of

问题 I have the following df : A B 2018-01-02 100.000000 100.000000 2018-01-03 100.808036 100.325886 2018-01-04 101.616560 102.307700 I am looking forward to change the time format of the index, so I tried (using @jezrael s response in the link Format pandas dataframe index date): df.index = rdo.index.strftime('%d-%m-%Y') But it outputs : AttributeError: 'Index' object has no attribute 'strftime' My desired output would be: A B 02-01-2018 100.000000 100.000000 03-01-2018 100.808036 100.325886 04

问题 I'm trying to solve a problem for a course in Python and found someone has implemented solutions for the same problem in github. I'm just trying to understand the solution given in github. I have a pandas dataframe called Top15 with 15 countries and one of the columns in the dataframe is 'HighRenew'. This column stores the % of renewable energy used in each country. My task is to convert the column values in 'HighRenew' column into boolean datatype. If the value for a particular country is

问题 I currently have the following data.csv which has a comma delimiter: name,day Chicken Sandwich,Wednesday Pesto Pasta,Thursday Lettuce, Tomato & Onion Sandwich,Friday Lettuce, Tomato & Onion Pita,Friday Soup,Saturday The parser script is: import pandas as pd df = pd.read_csv('data.csv', delimiter=',', error_bad_lines=False, index_col=False) print(df.head(5)) The output is: Skipping line 4: expected 2 fields, saw 3 Skipping line 5: expected 2 fields, saw 3 name day 0 Chicken Sandwich Wednesday