okdolly-001

select count(case when status='200 OK') as ok,count(case when status!='200 OK') as error from date_status group by time;

okdolly-001

import matplotlib.image as mpimg
import glob
import os
def load_data(dir_name = 'plates'):
    
    """ Your implementation """     
    data_path = os.path.join(dir_name, '*g')
    files = glob.glob(data_path)
    data = [mpimg.imread(img) for img in files]
    return data

okdolly-001

import pandas as pd

df = pd.DataFrame(
                    {
                      'AAA': [1,1,1,2,2,2,2,3,3],
                      'BBB': [2,1,3,4,5,1,2,3,7]
                    }

                  )
print(df,'\n')

okdolly-001

import os.path
import my_module

print(os.path.abspath(my_module.__file__))

okdolly-001

def get_one_hot(targets, nb_classes):
    return np.eye(nb_classes)[np.array(targets).reshape(-1)]
  
  
This is what happens:
np.eye(3)
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

okdolly-001

Set a subplots. Display image and title inside a for-loop.

%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow

fig, axs = plt.subplots(2,5, figsize=(15, 6), facecolor='w', edgecolor='k')
fig.subplots_adjust(hspace = .5, wspace=.001)

axs = axs.ravel()

okdolly-001

<img src="images/1.png" alt="Drawing" style="width: 800px;"/>

okdolly-001

correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
#tf.argmax finds the index of the largest value along axis = 1.tf.equal checks whether the two are equal, if the output index (predicted classes are the same, then correct prediction is 1).

okdolly-001

1.Remove duplicates in a list
cleanlist = []
for n in oldlist:
  if n not in cleanlist:
    cleanlist.append(n)

okdolly-001

1. Substitute the NaN's in a dataframe with values from another dataframe

If you have two DataFrames of the same shape, then:

df[df.isnull()] = d2



2.Replace values in a dataframe with values from another dataframe by conditions