fillna

问题 Discription: both features are in categorical dtypes. and i used this code in a different kernal of same dateset was working fine, the only difference is the features are in flote64. later i have converted these feature dtypes into Categorical because all the features in the dataset represents categories. Below is the code: AM_train['product_category_2'].fillna('Unknown', inplace =True) AM_train['city_development_index'].fillna('Missing', inplace =True) 回答1: Use Series.cat.add_categories for

问题 Discription: both features are in categorical dtypes. and i used this code in a different kernal of same dateset was working fine, the only difference is the features are in flote64. later i have converted these feature dtypes into Categorical because all the features in the dataset represents categories. Below is the code: AM_train['product_category_2'].fillna('Unknown', inplace =True) AM_train['city_development_index'].fillna('Missing', inplace =True) 回答1: Use Series.cat.add_categories for

问题 I have a dataframe with a column of sequential but not adjacent numbers and missing values. I'd like to use the fillna function to fill in the missing values with an incremented value from the previous non-missing row. Here's a simplified table: index my_counter 0 1 1 2 2 NaN 3 3 4 NaN 5 NaN 6 8 I'd like to fill in my_counter as such: index my_counter 0 1 1 2 2 2.1 3 3 4 3.1 5 3.2 6 8 How can I accomplish this task? 回答1: IIUC ffill with groupby cumcount df.my_counter.ffill()+df.groupby(df.my

问题 I want to implement a fillna method over a pandas dataframes with the method='bfill' and a limit labeled_features = final_feat.merge(failures, on=['datetime', 'machineID'], how='left') print(type(labeled_features)) labeled_features = labeled_features.bfill(limit=7) # fill backward up to 24h labeled_features = labeled_features.fillna('none') labeled_features.head() but I have the following error <class 'pandas.core.frame.DataFrame'> -------------------------------------------------------------

问题 For starters, here is some artificial data fitting my problem: df = pd.DataFrame(np.random.randint(0, 100, size=(vsize, 10)), columns = ["col_{}".format(x) for x in range(10)], index = range(0, vsize * 3, 3)) df_2 = pd.DataFrame(np.random.randint(0,100,size=(vsize, 10)), columns = ["col_{}".format(x) for x in range(10, 20, 1)], index = range(0, vsize * 2, 2)) df = df.merge(df_2, left_index = True, right_index = True, how = 'outer') df_tar = pd.DataFrame({"tar_1": [np.random.randint(0, 2) for

问题 need to fill the NA values with the past three values mean of that NA this is my dataset RECEIPT_MONTH_YEAR NET_SALES 0 2014-01-01 818817.20 1 2014-02-01 362377.20 2 2014-03-01 374644.60 3 2014-04-01 NA 4 2014-05-01 NA 5 2014-06-01 NA 6 2014-07-01 NA 7 2014-08-01 46382.50 8 2014-09-01 55933.70 9 2014-10-01 292303.40 10 2014-10-01 382928.60 回答1: is this dataset a .csv file or a dataframe. This NA is a 'NaN' or a string ? import pandas as pd import numpy as np df=pd.read_csv('your dataset',sep=

问题 I have a dataframe of integers. Preview (starts from 3 due to first 3 rows removal): The original data in the 'pixel1' column is int , but the NAN there forced it to float . I tried to fix it with: X_train.fillna(method='ffill', inplace=True) X_train = X_train.astype(int) print(X_train.head()) that results in: can I get the datatype of the value the fillna is using? is there a better way to do so? (better = to skip the astype step, as the data is int originally - I planted the NAN in the file

问题 I have a dataset and I want to fill the missing data in the column 'value' with bfill with adding a string to it. Here is to code that I have: import pandas as pd import numpy as np df = pd.DataFrame( { 'category': ['X', 'X', 'X', 'X', 'X', 'X', 'Y', 'Y', 'Y'], 'name': ['A','A', 'B','B','B','B', 'C','C','C'], 'other_value': [10, np.nan, np.nan, 20, 30, 10, 30, np.nan, 30], 'value': [1, np.nan, np.nan, 2, 3, 1, 3, np.nan, 3], } ) print(df) def fillValue(g): gNotNull = g.dropna() wtAvg = str

问题 I have a dataframe problem_data which has NaN values in some cells. I ran the following code. problem_data[problem_data['level_type'] == 5.0] It resulted to this : problem_id level_type points tags 5 prob_1479 5.0 NaN NaN 31 prob_2092 5.0 NaN NaN 38 prob_4395 5.0 NaN combinatorics,constructive algorithms,dfs 43 prob_5653 5.0 NaN NaN 48 prob_2735 5.0 2750.0 NaN 52 prob_1054 5.0 2000.0 combinatorics,dp 64 prob_2610 5.0 NaN NaN 65 prob_1649 5.0 NaN NaN 70 prob_4675 5.0 NaN dp,games 74 prob_445 5

问题 Here is my input: import pandas as pd import numpy as np list1 = [10,79,6,38,4,557,12,220,46,22,45,22] list2 = [4,3,23,6,234,47,312,2,426,42,435,23] df = pd.DataFrame({'A' : list1, 'B' : list2}, columns = ['A', 'B']) df['C'] = np.where (df['A'] > df['B'].shift(-2), 1, np.nan) print (df) that produces this output: A B C 0 10 4 NaN 1 79 3 1.0 2 6 23 NaN 3 38 6 NaN 4 4 234 NaN 5 557 47 1.0 6 12 312 NaN 7 220 2 1.0 8 46 426 NaN 9 22 42 NaN 10 45 435 NaN 11 22 23 NaN What I need to do is to change