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Articles on Trending Technologies
Technical articles with clear explanations and examples
Python - How to Group Pandas DataFrame by Days?
We will group Pandas DataFrame using the groupby(). Select the column to be used using the grouper function. We will group day-wise and calculate sum of Registration Price with day interval for our example shown below for Car Sale Records.Set the frequency as an interval of days in the groupby() grouper method, that means, if the freq is 7D, that would mean data grouped by interval of 7 days of every month till the last date given in the date column.At first, let’s say the following is our Pandas DataFrame with three columns −import pandas as pd # dataframe ...
Read MorePython - Grouping columns in Pandas Dataframe
To group columns in Pandas dataframe, use the groupby(). At first, let us create Pandas dataframe −dataFrame = pd.DataFrame( { "Car": ["Audi", "Lexus", "Audi", "Mercedes", "Audi", "Lexus", "Mercedes", "Lexus", "Mercedes"], "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350] } )Let us now group according to Car column −res = dataFrame.groupby("Car")After grouping, we will use functions to find the means Registration prices (Reg_Price) of grouped car names −res.mean()This calculates mean of the Registration price according to column Car.ExampleFollowing is the code −import pandas as pd # dataframe with one of ...
Read MorePython - Replace values of a DataFrame with the value of another DataFrame in Pandas
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 MorePython - Replace negative values with latest preceding positive value in Pandas DataFrame
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 MorePython - Drop specific rows from multiindex Pandas Dataframe
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 MorePython - Sum negative and positive values using GroupBy in Pandas
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 MorePython - How to Group Pandas DataFrame by Month?
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 MorePython – How to check missing dates in Pandas
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 MoreHow to do Fuzzy Matching on Pandas Dataframe Column Using Python?
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 MoreHow to count frequency of itemsets in Pandas DataFrame
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 ...
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