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Assume, you have a series and the numberic index with sorted distinct values are −Sorted distict values - numeric array index [2 3 0 3 2 1 4] ['apple' 'kiwi' 'mango' 'orange' 'pomegranate']To solve this, we will follow the steps given below −SolutionApply pd.factorize() function inside list of non-unique elements and save it as index, index_value.index, unique_value = pd.factorize(['mango', 'orange', 'apple', 'orange', 'mango', 'kiwi', 'pomegranate'])Print the index and elements. Result is diplayed without sorting of distinct values and its indexApply pd.factorize() inside list elements and set sort=True then save it as sorted_index, unique_valuesorted_index, unique_value = pd.factorize(['mango', 'orange', 'apple', 'orange', 'mango', ... Read More
Assume, you have a dataframe and the result for rolling window size 3 calculation is, Average of rolling window is: Id Age Mark 0 NaN NaN NaN 1 1.5 12.0 85.0 2 2.5 13.0 80.0 3 3.5 13.5 82.5 4 4.5 31.5 90.0 5 5.5 60.0 87.5To solve this, we will follow the below approach −SolutionDefine a dataframeApply df.rolling(window=2).mean() to calculate average of rolling window size 3 isdf.rolling(window=2).mean()ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({"Id":[1, 2, 3, 4, 5, 6], ... Read More
Assume, you have a series and the result for slicing substrings from each element in series as, 0 Ap 1 Oa 2 Mn 3 KwTo solve this, we will follow the below approaches −Solution 1Define a seriesApply str.slice function inside start=0, stop-4 and step=2 to slice the substring from the series.data.str.slice(start=0, stop=4, step=2)ExampleLet’s check the following code to get a better understanding −import pandas as pd data = pd.Series(['Apple', 'Orange', 'Mango', 'Kiwis']) print(data.str.slice(start=0, stop=4, step=2))Output0 Ap 1 Oa 2 Mn 3 KwSolution 2Define a seriesApply string index slice to start from 0 ... Read More
A sequential model can be built using Keras Sequential API that is used to work with plain stack of layers. Here every layer has exactly one input tensor and one output tensor.A pre-trained model can be used as the base model on the specific dataset. This saves the time and resources of having to train the model again on the specific dataset.A pre-trained model is a saved network which would be previously trained on a large dataset. This large dataset would be a large-scale image-classification task. A pre-trained model can be used as it is or it can be customized ... Read More
The result for splitting the string with ’' delimiter and convert to series as, 0 apple 1 orange 2 mango 3 kiwiTo solve this, we will follow the below approach −Solution 1define a function split_str() which accepts two arguments string and delimiterCreate s.split() function inside delimiter value and store it as split_datasplit_data = s.split(d)Apply split_data inside pd.Series() to generate series data.pd.Series(split_data)Finally, call the function to return the result.ExampleLet’s check the following code to get a better understanding −import pandas as pd def split_str(s, d): split_data = s.split(d) print(pd.Series(split_data)) split_str('apple\torange\tmango\tkiwi', '\t')Output0 apple 1 ... Read More
Assume, you have time series and the result for the first and last three days from the given series as, first three days: 2020-01-01 Chennai 2020-01-03 Delhi Freq: 2D, dtype: object last three days: 2020-01-07 Pune 2020-01-09 Kolkata Freq: 2D, dtype: objectTo solve this, we will follow the steps given below −SolutionDefine a series and store it as data.Apply pd.date_range() function inside start date as ‘2020-01-01’ and periods = 5, freq =’2D’ and save it as time_seriestime_series = pd.date_range('2020-01-01', periods = 5, freq ='2D')Set date.index = time_seriesPrint the first three days using data.first(’3D’) and save it ... Read More
Result for generating dataframe maximum by a minimum of each row is0 43.000000 1 1.911111 2 2.405405 3 20.000000 4 7.727273 5 6.333333To solve this, we will follow the steps given below −Solution 1Define a dataframe with size of 30 random elements from 1 to 100 and reshape the array by (6, 5) to change 2-D arraydf = pd.DataFrame(np.random.randint(1, 100, 30).reshape(6, 5))Create df.apply function inside lambda method to calculate np.max(x)/np.min(x) with axis as 1 and save as max_of_min. It is defined below, max_of_min = df.apply(lambda x: np.max(x)/np.min(x), axis=1)Finally print the max_of_minExampleLet’s check the following ... Read More
Assume, you have a dataframe and the result for second lowest value in each column as, Id 2 Salary 30000 Age 23To solve this, we will follow the steps given below −SolutionDefine a dataframeSet df.apply() function inside create lambda function and set the variable like x to access all columns and check expression asx.sort_values().unique()[1] with axis=0 to return second lowest value as, result = df.apply(lambda x: x.sort_values().unique()[1], axis=0)ExampleLet’s check the following code to get a better understanding −import pandas as pd df = pd.DataFrame({'Id':[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'Salary':[20000, 30000, 50000, ... Read More
Assume, you have a dataframe and the minimum number of missing value column is, DataFrame is: Id Salary Age 0 1.0 20000.0 22.0 1 2.0 NaN 23.0 2 3.0 50000.0 NaN 3 NaN 40000.0 25.0 4 5.0 80000.0 NaN 5 6.0 NaN 25.0 6 7.0 350000.0 26.0 7 8.0 55000.0 27.0 8 9.0 60000.0 NaN 9 10.0 70000.0 24.0 lowest missing value column is: IdTo solve this, we will follow the steps given ... Read More
Assume, you have a date_range of dates and the result for the total number of business days are, Dates are: DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16', '2020-01-17', '2020-01-20', '2020-01-21', '2020-01-22', '2020-01-23', '2020-01-24', '2020-01-27', '2020-01-28', '2020-01-29', '2020-01-30', '2020-01-31'], dtype='datetime64[ns]', freq='B') Total number of days: 23Solution 1Define a function as business_days()set pd.bdate_range() function start ... Read More