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The titanic dataset can be inspected using Tensorflow and estimators, by iterating through the features and converting the features into a list, and displaying it 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
A function that shuffles data can be defined, with the help of estimators. A dictionary is created that stores the data. This is done using the ‘from_tensor_slices’ 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 ... Read More
TensorFlow Hub is a repository that contains trained machine learning models. These models are ready to be fine-tuned and deployed anywhere. The trained models such as BERT and Faster R-CNN can be reused with a few lines of code. It is an open-repository, which means it can be used and modified by anyone.The tfhub.dev repository contains many pre-trained models. Some of them include text embeddings, image classification models, TF.js/TFLite models and so on.It can be installed using the below code:!pip install --upgrade tensorflow_hubIt can be imported into the working environment as shown in the below code:import tensorflow_hub as hub Read More
The result for splitting camel case strings into series as, enter the sring: pandasSeriesDataFrame Series is: 0 pandas 1 Series 2 Data 3 Frame dtype: objectTo solve this, we will follow the steps given below −SolutionDefine a function that accepts the input stringSet result variable with the condition as input is not lowercase and uppercase and no ’_’ in input string. It is defined below, result = (s != s.lower() and s != s.upper() and "_" not in s)Set if condition to check if the result is true the apply re.findall method to find camel case ... Read More
Assume, you have two series and the result for combining two series into dataframe as, Id Age 0 1 12 1 2 13 2 3 12 3 4 14 4 5 15To solve this, we can have three different approaches.Solution 1Define two series as series1 and series2Assign first series into dataframe. Store it as dfdf = pd.DataFrame(series1)Create a column df[‘Age’] in dataframe and assign second series inside to df.df['Age'] = pd.DataFrame(series2)ExampleLet’s check the following code to get a better understanding −import pandas as pd series1 = pd.Series([1, 2, 3, 4, 5], name='Id') series2 = pd.Series([12, 13, 12, 14, 15], name='Age') ... Read More
Assume, you have a dataframe and the result for a date, month, year column is, date day month year 0 17/05/2002 17 05 2002 1 16/02/1990 16 02 1990 2 25/09/1980 25 09 1980 3 11/05/2000 11 05 2000 4 17/09/1986 17 09 1986To solve this, we will follow the steps given below −SolutionCreate a list of dates and assign into dataframe.Apply str.split function inside ‘/’ delimiter to df[‘date’] column. Assign the result to df[[“day”, “month”, “year”]].ExampleLet’s check the following code to get a better understanding −import ... Read More
Assume, you have a series and the result for converting to dummy variable as, Female Male 0 0 1 1 1 0 2 0 1 3 1 0 4 0 1 5 0 0 6 1 0 7 1 0To solve this, we will follow the steps given below −SolutionCreate a list with ‘Male’ and ‘Female’ elements and assign into Series.Apply get_dummies function inside series and set dummy_na value as False. It is defined below, pd.get_dummies(series, dummy_na=False)ExampleLet’s check the following code to get ... Read More
Assume, you have a dataframe and the result for converted to latex as, \begin{tabular}{lrr} \toprule {} & Id & Age \ \midrule 0 & 1 & 12 \ 1 & 2 & 13 \ 2 & 3 & 14 \ 3 & 4 & 15 \ 4 & 5 & 16 \ \bottomrule \end{tabular}SolutionTo solve this, we will follow the steps given below −Define a dataframeApply to_latex() function to the dataframe and set index and multirow values as True. It is defined below, df.to_latex(index = True, multirow = True)ExampleLet’s ... Read More
The result for generating even length random four-digit pin numbers as, enter the series size 4 Random four digit pin number series 0 0813 1 7218 2 6739 3 8390To solve this, we will follow the steps given below −SolutionCreate an empty and list and set result as TrueSet while loop and get the size from the userSet if condition to find the size is even or odd. If the size is odd then assign the result as False and runs the loop until an even number is entered.l = [] while(True): size = int(input("enter ... Read More
Assume you have a dataframe, the result for removing unique prefix city names are, Id City 2 3 Kolkata 3 4 Hyderabad 6 7 Haryana 8 9 Kakinada 9 10 KochinTo solve this, we will follow the steps given below −SolutionDefine a dataframeCreate an empty list to append all the city column values first char’s, l = [] for x in df['City']: l.append(x[0])Create another empty list to filter repeated char.Set for loop and if condtion to append unique char’s. It is defined below, l1 = [] for j in l: if(l.count(j)>1): if(j not in ... Read More