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To replace values of a DataFrame with the value of another DataFrame, use the replace() method n Pandas.At first, let us first create a DataFrame −dataFrame1 = pd.DataFrame({"Car": ["Audi", "Lamborghini"], "Place": ["US", "UK"], "Units": [200, 500]})Let us create another DataFrame −dataFrame2 = pd.DataFrame({"Car": ["BMW", "Lexus"], "Place": ["India", "Australia"], "Units": [800, 1000]})Next, get a value from DataFrame2 and replace with a value from DataFrame1 −# get value from 2nd DataFrame i = dataFrame2['Car'][1] # replacing with a value from the 1st DataFrame j = dataFrame1['Car'][0]Finally, use the replace() method to replace the value of one DataFrame with value of another ... Read More
We want to replace the negative values with latest preceding positive value. With that, if there’s no positive preceding value, then the value should update to 0.InputFor example, the input is −DataFrame: One two 0 -2 -3 1 4 -7 2 6 5 3 0 -9OutputThe output should be − One two 0 0 0 1 7 0 2 4 2 3 0 2Data Frame masking is used to replace negative values. To fill the missing values, we used forward fill. At first, let ... Read More
To drop specific rows rom multiindex dataframe, use the drop() method. At first, let us create a multi-index array −arr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']), np.array(['valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC'])]Next, create multiindex dataframe and set index also −dataFrame = pd.DataFrame( np.random.randn(9, 3), index=arr, columns=['Col 1', 'Col 2', 'Col 3']) dataFrame.index.names = ['level 0', 'level 1']Now, drop specific row −dataFrame.drop(('car', 'valueA'), axis=0, inplace=True)ExampleFollowing is the code −import numpy as np import pandas as pd # multiindex array arr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']), ... Read More
Let us see how to find the sum of negative and positive values. At first, create a dataframe with positive and negative values −dataFrame = pd.DataFrame({'Place': ['Chicago', 'Denver', 'Atlanta', 'Chicago', 'Dallas', 'Denver', 'Dallas', 'Atlanta'], 'Temperature': [-2, 30, -5, 10, 30, -5, 20, -10]})Next, use groupby to group on the basis of Place column −groupRes = dataFrame.groupby(dataFrame['Place'])Use lambda function to return the positive and negative values. We have also added the positive and negative values individually −# lambda function def plus(val): return val[val > 0].sum() def minus(val): return val[val < 0].sum()ExampleFollowing is the complete code ... Read More
We will group Pandas DataFrame using the groupby. Select the column to be used using the grouper function. We will group month-wise and calculate sum of Registration Price monthly for our example shown below for Car Sale Records.At first, let’s say the following is our Pandas DataFrame with three columns −dataFrame = pd.DataFrame( { "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"], "Date_of_Purchase": [ pd.Timestamp("2021-06-10"), pd.Timestamp("2021-07-11"), pd.Timestamp("2021-06-25"), ... Read More
To check missing dates, at first, let us set a dictionary of list with date records i.e. Date of Purchase in our example −# dictionary of lists d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_purchase': ['2020-10-10', '2020-10-12', '2020-10-17', '2020-10-16', '2020-10-19', '2020-10-22']}Now, create a dataframe from the above dictionary of lists −dataFrame = pd.DataFrame(d)Next, set it as index −dataFrame = dataFrame.set_index('Date_of_purchase')Use to_datetime() to convert string to DateTime object −dataFrame.index = pd.to_datetime(dataFrame.index) Display remaining dates in a range −k = pd.date_range( start="2020-10-10", end="2020-10-22").difference(dataFrame.index);ExampleFollowing is the code −import pandas as pd # dictionary of lists d = {'Car': ['BMW', ... Read More
We will match words in the first DataFrame with words in the second DataFrame. For closest matches, we will use threshold. We took the value of threshold as 70 i.e., match occurs when the strings at more than 70% close to each other.Let us first create Dictionaries and convert to pandas dataframe −# dictionaries d1 = {'Car': ["BMW", "Audi", "Lexus", "Mercedes", "Rolls"]} d2 = {'Car': ["BM", "Audi", "Le", "MERCEDES", "Rolls Royce"]} # convert dictionaries to pandas dataframes df1 = pd.DataFrame(d1) df2 = pd.DataFrame(d2)Now, convert dataframe column to list of elements for fuzzy matching −myList1 = df1['Car'].tolist() myList2 = ... Read More
Use the Series.value_counts() method to count frequency of itemsets. At first, let us create a DataFrame −# Create DataFrame dataFrame = pd.DataFrame({'Car': ['BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Porsche', 'Lamborghini', 'BMW'], 'Place': ['Delhi', 'Hyderabad', 'Chandigarh', 'Bangalore', 'Hyderabad', 'Mumbai', 'Mumbai', 'Pune'], 'UnitsSold': [95, 80, 80, 75, 92, 90, 95, 50 ]})Count the frequency of column car using the value_counts() method −# counting frequency of column Car count1 = dataFrame['Car'].value_counts() print("Count in column Car") print(count1)In the same way, count the frequency of other columns. Following is the complete code to count frequency of itemsets in Pandas DataFrame ... Read More
The presence of congestion means the load is greater than the resources available over a network to handle. Generally we will get an idea to reduce the congestion by trying to increase the resources or decrease the load, but it is not that much of a good idea.Approaches to Congestion ControlThere are some approaches for congestion control over a network which are usually applied on different time scales to either prevent congestion or react to it once it has occurred.Let us understand these approaches step wise as mentioned below −Step 1 − The basic way to avoid congestion is to ... Read More
The network layer is concerned with getting packets from the source all the way to the destination with minimal cost. Unlike the data link layer which has more important goals like just moving frames from one end of a wire to another. The network layer is the lowest layer that deals with end-to-end transmission.Network Layer Design issuesNetwork layer Design issues are as follows −Store and forward packets switchingServices provided to the transport layerImplementation of connectionless servicesImplementation of connection oriented servicesComparison of virtual-circuit datagram networksNow let us see one of the design issues of the network layer.Services provided to the transport ... Read More