How To Create A Column In Python
In [1]: import pandas as pd
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Air quality data
For this tutorial, air quality data about \(NO_2\) is used, made available by openaq and using the py-openaq package. The
air_quality_no2.csvdata set provides \(NO_2\) values for the measurement stations FR04014, BETR801 and London Westminster in respectively Paris, Antwerp and London.To raw data
In [2]: air_quality = pd . read_csv ( "data/air_quality_no2.csv" , index_col = 0 , parse_dates = True ) In [3]: air_quality . head () Out[3]: station_antwerp station_paris station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN
How to create new columns derived from existing columns?¶
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I want to express the \(NO_2\) concentration of the station in London in mg/m\(^3\)
(If we assume temperature of 25 degrees Celsius and pressure of 1013 hPa, the conversion factor is 1.882)
In [4]: air_quality [ "london_mg_per_cubic" ] = air_quality [ "station_london" ] * 1.882 In [5]: air_quality . head () Out[5]: station_antwerp station_paris station_london london_mg_per_cubic datetime 2019-05-07 02:00:00 NaN NaN 23.0 43.286 2019-05-07 03:00:00 50.5 25.0 19.0 35.758 2019-05-07 04:00:00 45.0 27.7 19.0 35.758 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 2019-05-07 06:00:00 NaN 61.9 NaN NaN
To create a new column, use the
[]brackets with the new column name at the left side of the assignment.
Note
The calculation of the values is done element_wise. This means all values in the given column are multiplied by the value 1.882 at once. You do not need to use a loop to iterate each of the rows!
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I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column
In [6]: air_quality [ "ratio_paris_antwerp" ] = ( ...: air_quality [ "station_paris" ] / air_quality [ "station_antwerp" ] ...: ) ...: In [7]: air_quality . head () Out[7]: station_antwerp station_paris station_london london_mg_per_cubic ratio_paris_antwerp datetime 2019-05-07 02:00:00 NaN NaN 23.0 43.286 NaN 2019-05-07 03:00:00 50.5 25.0 19.0 35.758 0.495050 2019-05-07 04:00:00 45.0 27.7 19.0 35.758 0.615556 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN NaN
The calculation is again element-wise, so the
/is applied for the values in each row.
Also other mathematical operators ( + , - , \* , / ) or logical operators ( < , > , = ,…) work element wise. The latter was already used in the subset data tutorial to filter rows of a table using a conditional expression.
If you need more advanced logic, you can use arbitrary Python code via apply() .
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I want to rename the data columns to the corresponding station identifiers used by openAQ
In [8]: air_quality_renamed = air_quality . rename ( ...: columns = { ...: "station_antwerp" : "BETR801" , ...: "station_paris" : "FR04014" , ...: "station_london" : "London Westminster" , ...: } ...: ) ...:
In [9]: air_quality_renamed . head () Out[9]: BETR801 FR04014 London Westminster london_mg_per_cubic ratio_paris_antwerp datetime 2019-05-07 02:00:00 NaN NaN 23.0 43.286 NaN 2019-05-07 03:00:00 50.5 25.0 19.0 35.758 0.495050 2019-05-07 04:00:00 45.0 27.7 19.0 35.758 0.615556 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN NaN
The
rename()function can be used for both row labels and column labels. Provide a dictionary with the keys the current names and the values the new names to update the corresponding names.
The mapping should not be restricted to fixed names only, but can be a mapping function as well. For example, converting the column names to lowercase letters can be done using a function as well:
In [10]: air_quality_renamed = air_quality_renamed . rename ( columns = str . lower ) In [11]: air_quality_renamed . head () Out[11]: betr801 fr04014 london westminster london_mg_per_cubic ratio_paris_antwerp datetime 2019-05-07 02:00:00 NaN NaN 23.0 43.286 NaN 2019-05-07 03:00:00 50.5 25.0 19.0 35.758 0.495050 2019-05-07 04:00:00 45.0 27.7 19.0 35.758 0.615556 2019-05-07 05:00:00 NaN 50.4 16.0 30.112 NaN 2019-05-07 06:00:00 NaN 61.9 NaN NaN NaN
To user guide
Details about column or row label renaming is provided in the user guide section on renaming labels.
REMEMBER
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Create a new column by assigning the output to the DataFrame with a new column name in between the
[]. -
Operations are element-wise, no need to loop over rows.
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Use
renamewith a dictionary or function to rename row labels or column names.
How To Create A Column In Python
Source: https://pandas.pydata.org/docs/getting_started/intro_tutorials/05_add_columns.html
Posted by: robbfarome.blogspot.com
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