Server Side Programming Articles - Page 1271 of 2646

Input −Assume you have a DataFrame, and the result for transpose of index and columns are, Transposed DataFrame is   0 1 0 1 4 1 2 5 2 3 6Solution 1Define a DataFrameSet nested list comprehension to iterate each element in the two-dimensional list data and store it in result.result = [[data[i][j] for i in range(len(data))] for j in range(len(data[0]))Convert the result to DataFrame, df2 = pd.DataFrame(result)ExampleLet us see the complete implementation to get a better understanding −import pandas as pd data = [[1, 2, 3], [4, 5, 6]] df = pd.DataFrame(data) print("Original DataFrame is", df) result = [[data[i][j] ... Read More

Input −Assume you have a DataFrame, and the result for shifting the first column and fill the missing values are,  one two three 0 1   10 100 1 2   20 200 2 3   30 300 enter the value 15  one two three 0 15  1   10 1 15  2   20 2 15  3   30SolutionTo solve this, we will follow the below approach.Define a DataFrameShift the first column using below code, data.shift(periods=1, axis=1)Get the value from user and verify if it is divisible by 3 and 5. If the result is true then fill missing ... Read More

Input −Assume you have a series and default float quantilevalue is 3.0SolutionTo solve this, we will follow the steps given below −Define a SeriesAssign quantile default value .5 to the series and calculate the result. It is defined below,data.quantile(.5) ExampleLet us see the complete implementation to get a better understanding −import pandas as pd l = [10,20,30,40,50] data = pd.Series(l) print(data.quantile(.5))Output30.0

Input −Assume, we have a DataFrame and group the records based on the designation is −Designation architect    1 programmer   2 scientist    2SolutionTo solve this, we will follow the below approaches.Define a DataFrameApply groupby method for Designation column and calculate the count as defined below,df.groupby(['Designation']).count()ExampleLet us see the following implementation to get a better understanding.import pandas as pd data = { 'Id':[1,2,3,4,5],          'Designation': ['architect','scientist','programmer','scientist','programmer']} df = pd.DataFrame(data) print("DataFrame is",df) print("groupby based on designation:") print(df.groupby(['Designation']).count())OutputDesignation architect    1 programmer   2 scientist    2

Input −Assume, we have DataFrame with City and State columns and find the city, state name startswith ‘k’ and store into another CSV file as shown below −City, State Kochi, KeralaSolutionTo solve this, we will follow the steps given below.Define a DataFrameCheck the city starts with ‘k’ as defined below, df[df['City'].str.startswith('K') & df['State'].str.startswith('K')] Finally, store the data in the ‘CSV’ file as below, df1.to_csv(‘test.csv’)ExampleLet us see the following implementation to get a better understanding.import pandas as pd import random as r data = { 'City': ['Chennai', 'Kochi', 'Kolkata'], 'State': ['Tamilnad', 'Kerala', 'WestBengal']} df = pd.DataFrame(data) print("DataFrame is", df) df1 = ... Read More

Input −Assume, sample DataFrame is,  Id Name 0 1 Adam 1 2 Michael 2 3 David 3 4 Jack 4 5 PeterOutputput −Random row is   Id    5 Name PeterSolutionTo solve this, we will follow the below approaches.Define a DataFrameCalculate the number of rows using df.shape[0] and assign to rows variable.set random_row value from randrange method as shown below.random_row = r.randrange(rows)Apply random_row inside iloc slicing to generate any random row in a DataFrame. It is defined below, df.iloc[random_row, :]ExampleLet us see the following implementation to get a better understanding.import pandas as pd import random as r data = { ... Read More

The model can be trained using the ‘train’ method in Tensorflow, where the epochs (number of times the data has to be trained to fit the model) and the training data are specified.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.print("The model is being trained") epochs=12 history = model.fit(    train_ds,   ... Read More

The created model in Tensorflow can be compiled using the ‘compile’ method. The loss is calculated using the ‘SparseCategoricalCrossentropy’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.print("The model is being compiled") model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),    metrics=['accuracy']) print("The architecture of the model") model.summary()Code credit: https://www.tensorflow.org/tutorials/images/classificationOutputThe model is being compiled The architecture of the ... Read More

A sequential model can be created using the ‘Sequential’ API that uses the ‘ layers.experimental.preprocessing.Rescaling’ method. The other layers are specified while created the model.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.We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero ... Read More

We will be using the flowers dataset, which contains images of several thousands of flowers. It contains 5 sub-directories, and there is one sub-directory for every class. Once the flower dataset has been downloaded using the ‘get_file’ method, it will be loaded into the environment to work with it.The flower data can be standardized by introducing a normalization layer in the model. This layer is called the ‘Rescaling’ layer, which is applied to the entire dataset using the ‘map’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We are using the Google Colaboratory to ... Read More