Server Side Programming Articles - Page 1265 of 2646

Augmentation can be used to reduce overfitting by adding additional training data. This is done by creating a sequential model that uses a ‘RandomFlip’ layer.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 ... Read More

The training results can be visualized with Tensorflow using Python with the help of the ‘matplotlib’ library. The ‘plot’ method is used to plot the data on the console.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 ... Read More

The result for splitting the string with ’' delimiter and convert to series as, 0    apple 1    orange 2    mango 3    kiwiTo solve this, we will follow the below approach −Solution 1define a function split_str() which accepts two arguments string and delimiterCreate s.split() function inside delimiter value and store it as split_datasplit_data = s.split(d)Apply split_data inside pd.Series() to generate series data.pd.Series(split_data)Finally, call the function to return the result.ExampleLet’s check the following code to get a better understanding −import pandas as pd def split_str(s, d):    split_data = s.split(d)    print(pd.Series(split_data)) split_str('apple\torange\tmango\tkiwi', '\t')Output0    apple 1   ... Read More

Assume, you have time series and the result for the first and last three days from the given series as, first three days: 2020-01-01    Chennai 2020-01-03    Delhi Freq: 2D, dtype: object last three days: 2020-01-07    Pune 2020-01-09    Kolkata Freq: 2D, dtype: objectTo solve this, we will follow the steps given below −SolutionDefine a series and store it as data.Apply pd.date_range() function inside start date as ‘2020-01-01’ and periods = 5, freq =’2D’ and save it as time_seriestime_series = pd.date_range('2020-01-01', periods = 5, freq ='2D')Set date.index = time_seriesPrint the first three days using data.first(’3D’) and save it ... Read More

Result for generating dataframe maximum by a minimum of each row is0    43.000000 1    1.911111 2    2.405405 3    20.000000 4    7.727273 5    6.333333To solve this, we will follow the steps given below −Solution 1Define a dataframe with size of 30 random elements from 1 to 100 and reshape the array by (6, 5) to change 2-D arraydf = pd.DataFrame(np.random.randint(1, 100, 30).reshape(6, 5))Create df.apply function inside lambda method to calculate np.max(x)/np.min(x) with axis as 1 and save as max_of_min. It is defined below, max_of_min = df.apply(lambda x: np.max(x)/np.min(x), axis=1)Finally print the max_of_minExampleLet’s check the following ... Read More

Assume, you have a dataframe and the result for second lowest value in each column as, Id       2 Salary 30000 Age    23To solve this, we will follow the steps given below −SolutionDefine a dataframeSet df.apply() function inside create lambda function and set the variable like x to access all columns and check expression asx.sort_values().unique()[1] with axis=0 to return second lowest value as, result = df.apply(lambda x: x.sort_values().unique()[1], axis=0)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, 7, 8, 9, 10], 'Salary':[20000, 30000, 50000, ... Read More

Assume, you have a dataframe and the minimum number of missing value column is, DataFrame is:    Id    Salary     Age 0 1.0    20000.0   22.0 1 2.0    NaN       23.0 2 3.0    50000.0   NaN 3 NaN    40000.0   25.0 4 5.0    80000.0   NaN 5 6.0    NaN       25.0 6 7.0    350000.0  26.0 7 8.0    55000.0   27.0 8 9.0    60000.0   NaN 9 10.0   70000.0   24.0 lowest missing value column is: IdTo solve this, we will follow the steps given ... Read More

Assume, you have a date_range of dates and the result for the total number of business days are, Dates are: DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-06',                '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10',                '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16',                '2020-01-17', '2020-01-20', '2020-01-21', '2020-01-22',                '2020-01-23', '2020-01-24', '2020-01-27', '2020-01-28',                '2020-01-29', '2020-01-30', '2020-01-31'],                dtype='datetime64[ns]', freq='B') Total number of days: 23Solution 1Define a function as business_days()set pd.bdate_range() function start ... Read More

Assume, you have a dataframe and the result for flatten records in C and F order as, flat c_order:    [10 12 25 13 3 12 11 14 24 15 6 14] flat F_order:    [10 25 3 11 24 6 12 13 12 14 15 14]SolutionTo solve this, we will follow the steps given below −Define a dataframeApply df.values.ravel() function inside set an argument as order=’C’ and save it as C_order, C_order = df.values.ravel(order='C')Apply df.values.ravel() function inside set an argument as order=’F’ and save it as F_order, F_order = df.values.ravel(order='F')ExampleLet’s check the following code to get a better understanding ... Read More

Assume, you have a dataframe and the result for orderDict with list of tuples are −OrderedDict([('Index', 0), ('Name', 'Raj'), ('Age', 13), ('City', 'Chennai'), ('Mark', 80)]) OrderedDict([('Index', 1), ('Name', 'Ravi'), ('Age', 12), ('City', 'Delhi'), ('Mark', 90)]) OrderedDict([('Index', 2), ('Name', 'Ram'), ('Age', 13), ('City', 'Chennai'), ('Mark', 95)])SolutionTo solve this, we will follow the steps given below −Define a dataframeSet for loop to access all the rows using df.itertuples() function inside set name=’stud’for row in df.itertuples(name='stud')Convert all the rows to orderDict with list of tuples using rows._asdict() function and save it as dict_row. Finally print the values, dict_row = row._asdict() print(dict_row)ExampleLet’s check the ... Read More