Server Side Programming Articles - Page 1264 of 2650

The result for converting celsius to Fahrenheit as,  Id Celsius Fahrenheit 0 1  37.5    99.5 1 2  36.0    96.8 2 3  40.0    104.0 3 4  38.5    101.3 4 5  39.0    102.2To solve this, we will follow below approaches −Solution 1Define a dataframe with ‘Id’ and ‘Celsius’ column valuesApply df.assign function inside write lambda function to convert celsius values by multiplying (9/5)*df[celsius]+32 and assign it to Fahrenheit. It is defined below −df.assign(Fahrenheit = lambda x: (9/5)*x['Celsius']+32)ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({'Id':[1, 2, 3, 4, 5], ... Read More

The result for appending magic numbers from 1 to 100 is, magic number series: 0       1 1       10 2       19 3       28 4       37 5       46 6       55 7       64 8       73 9       82 10      91 11     100To solve this, we will follow the below approaches −Solution 1Create list comprehension to append 1 to 100 values to list ls.ls = [i for i in range(1, 101)]Apply ... Read More

Result for printing palindrome names are −Palindrome names are:    Id   Name 0   1   bob 2   3   hannahTo solve this, we will follow the below approaches −Solution 1Define a dataframeCreate list comprehension inside set for loop to access all the values from df[‘Name’] column using i variable and set if condition to compare i==i[::-1] then add i value to the listl = [ i for i in df['Name'] if(i==i[::-1])]Finally, check the list values present in the df[‘Name’] column using isin()df[df['Name'].isin(l)]ExampleLet’s check the following code to get a better understanding −import pandas as pd data = ... Read More

The augmented model can be compiled using the ‘compile’ method, which also takes ‘SparseCategoricalCrossentropy’ as parameter to calculate the loss associated with training.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model. We are ... Read More

Tensorflow can be used to reduce overfitting using dropout technique where a sequential model is created that consists of a Rescaling layer, and the augmented data as its layers.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural ... Read More

The augmented data can be visualized using Tensorflow and Python with the help of ‘matplotlib’ library. The images are iterated over, and plotted using ‘imshow’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build ... Read More

Assume, you have a time series and the result for localize asian time zone as, Index is: DatetimeIndex(['2020-01-05 00:30:00+05:30', '2020-01-12 00:30:00+05:30',                '2020-01-19 00:30:00+05:30', '2020-01-26 00:30:00+05:30',                '2020-02-02 00:30:00+05:30'],                dtype='datetime64[ns, Asia/Calcutta]', freq='W-SUN')SolutionDefine a dataframeCreate time series using pd.date_range() function with start as ‘2020-01-01 00:30’, periods=5 and tz = ‘Asia/Calcutta’ then store it as time_index.time_index = pd.date_range('2020-01-01 00:30', periods = 5, freq ='W', tz = 'Asia/Calcutta')Set df.index to store localized time zone from time_indexdf.index = time_indexFinally print the localized timezoneExampleLet’s check the ... Read More

Assume, you have datetime column in dataframe and the result for separating date and time as,    datetime    date          time 0 2020-01-01 07:00:00 2020-01-06 07:00:00 1 2020-01-02 07:00:00 2020-01-06 07:00:00 2 2020-01-03 07:00:00 2020-01-06 07:00:00 3 2020-01-04 07:00:00 2020-01-06 07:00:00 4 2020-01-05 07:00:00 2020-01-06 07:00:00 5 2020-01-06 07:00:00 2020-01-06 07:00:00To solve this, we will follow the below approaches −Solution 1Define a dataframe ‘datetime’ column using pd.date_range(). It is defined below, pd.DataFrame({'datetime':pd.date_range('2020-01-01 07:00', periods=6)})Set for loop d variable to access df[‘datetime’] column one by one.Convert date and time from for loop and save it as df[‘date’] ... Read More

Assume, you have a series and the numberic index with sorted distinct values are −Sorted distict values - numeric array index [2 3 0 3 2 1 4] ['apple' 'kiwi' 'mango' 'orange' 'pomegranate']To solve this, we will follow the steps given below −SolutionApply pd.factorize() function inside list of non-unique elements and save it as index, index_value.index, unique_value = pd.factorize(['mango', 'orange', 'apple', 'orange', 'mango', 'kiwi', 'pomegranate'])Print the index and elements. Result is diplayed without sorting of distinct values and its indexApply pd.factorize() inside list elements and set sort=True then save it as sorted_index, unique_valuesorted_index, unique_value = pd.factorize(['mango', 'orange', 'apple', 'orange', 'mango', ... Read More

Assume, you have a dataframe and the result for rolling window size 3 calculation is, Average of rolling window is:    Id Age  Mark 0 NaN NaN  NaN 1 1.5 12.0 85.0 2 2.5 13.0 80.0 3 3.5 13.5 82.5 4 4.5 31.5 90.0 5 5.5 60.0 87.5To solve this, we will follow the below approach −SolutionDefine a dataframeApply df.rolling(window=2).mean() to calculate average of rolling window size 3 isdf.rolling(window=2).mean()ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, 4, 5, 6],                     ... Read More