@shubham851 wrote:
in case of technique#3, while executing following code i am getting error, how do i rectify it
import pandas as pd import numpy as np import matplotlib as plt df=pd.read_csv("/Users/choudharyshubham6789/ANALYTICS VIDHYA/train.csv") from scipy.stats import mode df['Gender'].fillna(mode(df['Gender']).mode[0], inplace=True) df['Married'].fillna(mode(df['Married']).mode[0], inplace=True) df['Self_Employed'].fillna(mode(df['Self_Employed']).mode[0], inplace=True) def mn(x): return sum(x.isnull()) print("the missing values in column are:") print(df.apply(mn, axis=0)) print("\n the missing values in column are:") print(df.apply(mn, axis=1))
the error is
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:245: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored. "values. nan values will be ignored.", RuntimeWarning) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-20-7710b3b5f55e> in <module>() 10 #print(df.apply(mn, axis=1)) 11 from scipy.stats import mode ---> 12 df['Gender'].fillna(mode(df['Gender']).mode[0], inplace=True) 13 df['Married'].fillna(mode(df['Married']).mode[0], inplace=True) 14 df['Self_Employed'].fillna(mode(df['Self_Employed']).mode[0], inplace=True) C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py in mode(a, axis, nan_policy) 437 return mstats_basic.mode(a, axis) 438 --> 439 scores = np.unique(np.ravel(a)) # get ALL unique values 440 testshape = list(a.shape) 441 testshape[axis] = 1 C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py in unique(ar, return_index, return_inverse, return_counts, axis) 221 ar = np.asanyarray(ar) 222 if axis is None: --> 223 return _unique1d(ar, return_index, return_inverse, return_counts) 224 if not (-ar.ndim <= axis < ar.ndim): 225 raise ValueError('Invalid axis kwarg specified for unique') C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py in _unique1d(ar, return_index, return_inverse, return_counts) 281 aux = ar[perm] 282 else: --> 283 ar.sort() 284 aux = ar 285 flag = np.concatenate(([True], aux[1:] != aux[:-1])) TypeError: '<' not supported between instances of 'str' and 'float'
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