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    In [1]: import pandas as pd

Data used for this tutorial:

- 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.csv` data 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

- 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  ...  london_mg_per_cubic
      datetime                              ...
      2019-05-07 02:00:00              NaN  ...               43.286
      2019-05-07 03:00:00             50.5  ...               35.758
      2019-05-07 04:00:00             45.0  ...               35.758
      2019-05-07 05:00:00              NaN  ...               30.112
      2019-05-07 06:00:00              NaN  ...                  NaN

      [5 rows x 4 columns]

  To create a new column, use the square 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!

- 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  ...  ratio_paris_antwerp
      datetime                              ...
      2019-05-07 02:00:00              NaN  ...                  NaN
      2019-05-07 03:00:00             50.5  ...             0.495050
      2019-05-07 04:00:00             45.0  ...             0.615556
      2019-05-07 05:00:00              NaN  ...                  NaN
      2019-05-07 06:00:00              NaN  ...                  NaN

      [5 rows x 5 columns]

  The calculation is again element-wise, so the `/` is applied for the values in each row.

Other mathematical operators (`+`, `-`, `*`, `/`, …) and logical operators (`<`, `>`, `==`, …) also 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()`.

- 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_mg_per_cubic  ratio_paris_antwerp
      datetime                               ...
      2019-05-07 02:00:00      NaN      NaN  ...               43.286                  NaN
      2019-05-07 03:00:00     50.5     25.0  ...               35.758             0.495050
      2019-05-07 04:00:00     45.0     27.7  ...               35.758             0.615556
      2019-05-07 05:00:00      NaN     50.4  ...               30.112                  NaN
      2019-05-07 06:00:00      NaN     61.9  ...                  NaN                  NaN

      [5 rows x 5 columns]

  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_mg_per_cubic  ratio_paris_antwerp
    datetime                               ...
    2019-05-07 02:00:00      NaN      NaN  ...               43.286                  NaN
    2019-05-07 03:00:00     50.5     25.0  ...               35.758             0.495050
    2019-05-07 04:00:00     45.0     27.7  ...               35.758             0.615556
    2019-05-07 05:00:00      NaN     50.4  ...               30.112                  NaN
    2019-05-07 06:00:00      NaN     61.9  ...                  NaN                  NaN

    [5 rows x 5 columns]

To user guide

Details about column or row label renaming is provided in the user guide section on renaming labels.

#### REMEMBER

- 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.

- Use `rename` with a dictionary or function to rename row labels or column names.

To user guide

The user guide contains a separate section on column addition and deletion.