Category : dtype

import pandas as pd import numpy as np data_A=pd.read_csv(‘D:/data_A.csv’) data_A has a column named time. dtype of time is int64. But after I run the code below, timehas changed to float type. data_A.loc[data_A[‘scan’] == 1., data_A.columns.difference( [‘scan’, ‘label’, ‘level’,’index’])] = np.nan data_A.loc[data_A[‘NH4’] < 0., ‘NH4’] = np.nan data_A.loc[data_A[‘NH4’] > 10., ‘NH4’] = np.nan data_A.loc[data_A[‘NH4_Y’]<0, ‘NH4_Y’] ..

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I have a pandas dataframe which has byte strings as elements in a column: E.g. b’hey’. When I write this dataframe to a csv and read if afterwards, pandas will return a string with the following form "b’hey’". This is a problem, because when calling tf.data.Dataset.from_tensor_slices the string will be casted to a byte string ..

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My data set is called book. I have changed 3 object columns (season, days of week, genre) to numbers using the following code for all: book.season[book.season == ‘Winter’] = 0 book.season[book.season == ‘Spring’] = 1 book.season[book.season == ‘Summer’] = 2 book.season[book.season == ‘Fall’] = 3 When I run the set, it shows the numbers in ..

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def train(self): self.P = np.random.rand(self.nr_users, self.K)/self.K self.Q = np.random.rand(self.nr_events, self.K)/self.K self.b_u = np.zeros(self.nr_users, dtype=’uint8′) self.b_m = np.zeros(self.nr_events, dtype=’uint8′) self.b = np.uint8(np.mean(self.R[np.where(self.R != 0)])) numpy.core._exceptions.MemoryError: Unable to allocate 10.3 GiB for an array with shape (138494, 10000) and data type float64 Unable to get rid of this error. Tried the .astype=(‘uint8′) does not work. My python ..

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I have two dataframes of size (5, 5) one of dtype int64 and another of type pd.Int64Dtype. np.random.seed(2021) data = np.arange(25).reshape((5, 5)) one = pd.DataFrame(data, dtype=’int64′) two = pd.DataFrame(data.copy(), dtype=’Int64′) # Notice the capital ‘I’ r, c = np.random.randint(0, 5, (2, 5)) The problem occurs when I try to change the underlying data. one.to_numpy()[r, c] ..

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