Programming Articles - Page 1413 of 3366

Assume you have a sqlite3 database with student records and the result for reading all the data is,   Id Name 0 1 stud1 1 2 stud2 2 3 stud3 3 4 stud4 4 5 stud5SolutionTo solve this, we will follow the steps given below −Define a new connection. It is shown below, con = sqlite3.connect("db.sqlite3")Read sql data from the database using below function, pd.read_sql_query()Select all student data from table using read_sql_query with connection, pd.read_sql_query("SELECT * FROM student", con)ExampleLet us see the complete implementation to get a better understanding −import pandas as pd import sqlite3 con = sqlite3.connect("db.sqlite3") df = ... Read More

Assume you have a series and the result for Boolean operations, And operation is: 0    True 1    True 2    False dtype: bool Or operation is: 0    True 1    True 2    True dtype: bool Xor operation is: 0    False 1    False 2    True dtype: boolSolutionTo solve this, we will follow the below approach.Define a SeriesCreate a series with boolean and nan valuesPerform boolean True against bitwise & operation to each element in the series defined below, series_and = pd.Series([True, np.nan, False], dtype="bool") & TruePerform boolean True against bitwise | operation ... Read More

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