Python Forum

When trying to convert a column to datetime format I get this error.

Error:
|Could not convert object to NumPy datetime|

I am trying the below:

X_test = pd.DataFrame(np.array(
    [['2020-04-03T00:00:00.000000000'],
       ['2020-04-07T00:00:00.000000000'],
       ['2020-04-06T00:00:00.000000000'],
       ...,
       ['2020-04-01T00:00:00.000000000'],
       ['2020-04-03T00:00:00.000000000'],
       ['2020-03-27T00:00:00.000000000']], dtype='datetime64[ns]'))

X_test.columns = ["Merged"]
X_test['Merged'] = pd.to_date(X_test['Merged'])
X_test['Merged'] = X_test['Merged'].map(dt.datetime.toordinal)

Please can you suggest me?

GITHUB:
https://github.com/rajeevjagatap/Python_...nalysis.py

also, please post complete unaltered error traceback (always) in error tags.
It contains very important information on what led up to and where error occurred.

Below is the entire code and error.

#!/usr/bin/env python
# coding: utf-8

# # COVID Analyis

# In[33]:

import pandas as pd
import datetime as dt
from pandas import datetime
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sea
from sklearn import linear_model
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')

rawdf = pd.read_excel('covidrawdata.xlsx', header=0, parse_dates=[0], index_col=0, squeeze=True,)
from sklearn.linear_model import LinearRegression

### Select required Fields
# In[3]:

rawdf1 = rawdf.loc[:,("Patient Number", "Merged")]#, "Week of Year")]
#rawdf1['Week of Year'] = pd.to_numeric(rawdf1['Week of Year'])
rawdf1

# In[ ]:

# In[65]:

rawdf1 = rawdf1.dropna()
rawdf1['Merged'] = rawdf1['Merged']
rawdf1['Merged'] = pd.to_datetime(rawdf1['Merged'])
rawdf1['Patient Number'] = rawdf1['Patient Number'].apply(np.int64)
rawdf1.info()

# In[6]:

plt.plot(rawdf1['Merged'], rawdf1['Patient Number'], color = 'red')
plt.xlabel("Week")
plt.ylabel("Patients")

# In[7]:

plt.bar(rawdf1['Merged'], rawdf1['Patient Number'])
plt.xlabel("Date")
plt.ylabel("Patients")

# In[19]:

plt.show()

# In[8]:

sea.distplot(rawdf1['Merged'])

# In[ ]:

# In[49]:

reg = linear_model.LinearRegression()
y = rawdf1['Patient Number'].values.reshape(-1,1)
X = rawdf1['Merged'].values.reshape(-1,1)

# In[68]:

print(reg)

# In[50]:

reg.fit(X,y)

# In[51]:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=0)

# In[52]:

regressor = LinearRegression()

# In[54]:

regressor.fit(X_train, y_train)

# In[55]:

print(regressor.intercept_)

# In[56]:

print(regressor.coef_)

# In[58]:

X_test

# In[78]:

import datetime as dt

X_test = pd.DataFrame(np.array(
[['2020-04-03T00:00:00.000000000'],
['2020-04-07T00:00:00.000000000'],
['2020-04-06T00:00:00.000000000'],
...,
['2020-04-01T00:00:00.000000000'],
['2020-04-03T00:00:00.000000000'],
['2020-03-27T00:00:00.000000000']], dtype='datetime64[ns]'))

X_test.columns = ["Merged"]
X_test['Merged'] = pd.to_date(X_test['Merged'])
X_test['Merged'] = X_test['Merged'].map(dt.datetime.toordinal)

Error:---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-78-c5cfb0bc2381> in <module>
      8        ['2020-04-01T00:00:00.000000000'],
      9        ['2020-04-03T00:00:00.000000000'],
---> 10        ['2020-03-27T00:00:00.000000000']], dtype='datetime64[ns]'))
     11 
     12 X_test.columns = ["Merged"]

ValueError: Could not convert object to NumPy datetime

# In[77]:

y_pred = regressor.predict(X_test)

# In[ ]:

#df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})
#df

# In[ ]:

#plt.scatter(X_test, y_test, color='gray')
#plt.plot(X_test, y_pred, color='red', linewidth=2)
#plt.show()

# In[ ]:

#print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))
#print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))
#print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

# In[ ]:

rajeevjagatap

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rajeevjagatap