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Found 507 Articles for Pandas
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
132 Views
Assume, you have a dataframe and the shift index by two periods in positive and negative direction is, shift the index by three periods in positive direction Id Age 2020-01-01 00:00:00 NaN NaN 2020-01-01 12:00:00 NaN NaN 2020-01-02 00:00:00 1.0 10.0 2020-01-02 12:00:00 2.0 12.0 2020-01-03 00:00:00 3.0 14.0 shift the index by three periods in negative direction Id Age 2020-01-01 00:00:00 3.0 14.0 2020-01-01 12:00:00 4.0 11.0 2020-01-02 00:00:00 5.0 13.0 2020-01-02 12:00:00 NaN NaN 2020-01-03 00:00:00 NaN NaNSolutionTo ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
231 Views
Assume, you have a dataframe and the result for removing first duplicate rows are, Id Age 0 1 12 3 4 13 4 5 14 5 6 12 6 2 13 7 7 16 8 3 14 9 9 15 10 10 14SolutionTo solve this, we will follow the steps given below −Define a dataframeApply drop_duplicates function inside Id and Age column then assign keep initial value as ‘last’.df.drop_duplicates(subset=['Id', 'Age'], keep='last')Store the result inside same dataframe and print itExampleLet’s see the below implementation to get a better understanding −import pandas ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
260 Views
Assume, you have a dataframe and the result for calculating covariance from grouped data and corresponding column as, Grouped data covariance is: mark1 mark2 subjects maths mark1 25.0 12.500000 mark2 12.5 108.333333 science mark1 28.0 50.000000 mark2 50.0 233.333333 Grouped data covariance between two columns: subjects maths 12.5 science 50.0 dtype: float64SolutionTo solve this, we will follow the steps given below −Define a dataframeApply groupby function inside dataframe subjects ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
364 Views
We can reshape a dataframe using melt(), stack(), unstack() and pivot() function.Solution 1Define a dataframe.Apply melt() function to convert wide dataframe column as rows. It is defined below, df.melt()ExampleLet’s see the below code to get a better understanding −import pandas as pd df = pd.DataFrame({'Id':[1, 2, 3], 'Age':[13, 14, 13], 'Mark':[80, 90, 85]}) print("Dataframe is:", df) print(df.melt())OutputDataframe is: Id Age Mark 0 1 13 80 1 2 14 90 2 3 13 85 variable value 0 Id 1 1 Id 2 2 Id 3 3 Age 13 4 ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
237 Views
Assume you have a dataframe with time series data and the result for truncated data is, before truncate: Id time_series 0 1 2020-01-05 1 2 2020-01-12 2 3 2020-01-19 3 4 2020-01-26 4 5 2020-02-02 5 6 2020-02-09 6 7 2020-02-16 7 8 2020-02-23 8 9 2020-03-01 9 10 2020-03-08 after truncate: Id time_series 1 2 2020-01-12SolutionTo solve this, we will follow the steps given below −Define a dataframe.Create date_range function inside start=’01/01/2020’, periods = 10 and assign freq = ‘W’. It will generate ten dates from given start date to next weekly start dates and store it as df[‘time_series’].df['time_series'] ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
228 Views
Assume, you have series and the result for autocorrelation with lag 2 is, Series is: 0 2.0 1 10.0 2 3.0 3 4.0 4 9.0 5 10.0 6 2.0 7 NaN 8 3.0 dtype: float64 series correlation: -0.4711538461538461 series correlation with lags: -0.2933396642805515SolutionTo solve this, we will follow the steps given below −Define a seriesFind the series autocorrelation using the below method, series.autocorr()Calculate the autocorrelation with lag=2 as follows, series.autocorr(lag=2)ExampleLet’s see the below code to get a better understanding, import pandas as pd import numpy as np series = ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
139 Views
Assume you have a dataframe and the result for exporting into pickle file and read the contents from file as, Export to pickle file: Read contents from pickle file: Fruits City 0 Apple Shimla 1 Orange Sydney 2 Mango Lucknow 3 Kiwi WellingtonSolutionTo solve this, we will follow the steps given below −Define a dataframe.Export the dataframe to pickle format and name it as ‘pandas.pickle’, df.to_pickle('pandas.pickle')Read the contents from ‘pandas.pickle’ file and store it as result, result = pd.read_pickle('pandas.pickle')ExampleLet’s see the below implementation to get better understanding, import pandas as pd df = pd.DataFrame({'Fruits': ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
930 Views
Assume you have the following sample json data stored in a file as pandas_sample.json{ "employee": { "name": "emp1", "salary": 50000, "age": 31 } }The result for after converting to csv as, , employee age, 31 name, emp1 salary, 50000SolutionTo solve this, we will follow the steps given below −Create pandas_sample.json file and store the JSON data.Read json data from the file and store it as data.data = pd.read_json('pandas_sample.json')Convert the data into dataframedf = pd.DataFrame(data)Apple df.to_csv function to convert the data as csv file format, df.to_csv('pandas_json.csv')ExampleLet’s see the below implementation ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
135 Views
Assume, you have time series and the result for maximum month-end frequency, DataFrame is: Id time_series 0 1 2020-01-05 1 2 2020-01-12 2 3 2020-01-19 3 4 2020-01-26 4 5 2020-02-02 5 6 2020-02-09 6 7 2020-02-16 7 8 2020-02-23 8 9 2020-03-01 9 10 2020-03-08 Maximum month end frequency: Id time_series time_series 2020-01-31 4 2020-01-26 2020-02-29 8 2020-02-23 2020-03-31 10 2020-03-08SolutionTo solve this, we will follow the steps given below −Define a dataframe with one column, d = {'Id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]} ... Read More
![Vani Nalliappan](https://www.tutorialspoint.com/assets/profiles/304793/profile/60_62256-1613462273.jpg)
1K+ Views
Assume, we have already saved pandas.csv file and export the file to Html formatSolutionTo solve this, we will follow the steps given below −Read the csv file using the read_csv method as follows −df = pd.read_csv('pandas.csv')Create new file pandas.html in write mode using file object, f = open('pandas.html', 'w')Declare result variable to convert dataframe to html file format, result = df.to_html()Using the file object, write all the data from the result. Finally close the file object, f.write(result) f.close()ExampleLet’s see the below implementation to get a better understanding −import pandas as pd df = pd.read_csv('pandas.csv') print(df) f = open('pandas.html', 'w') result ... Read More