Data wrangling, grouping and aggregation#

Next, we will continue working with weather data, but expand our analysis to cover longer periods of data from Finland. In the following, you will learn various useful techniques in pandas to manipulate, group and aggregate the data in different ways that are useful when extracting information from your data. In the end, you will learn how to create an automated data analysis workflow that can be repeated with multiple input files that have a similar structure. As a case study, we will investigate whether January 2020 was the warmest month on record in Finland, as it was the warmest on record globally at the time [1].

Cleaning data while reading#

In this section we are using weather observation data from Finland that was downloaded from NOAA (check the data overview section for more details). The input data are separated by varying number of spaces (i.e., fixed column widths). The first lines of the data look like following:

  USAF  WBAN YR--MODAHRMN DIR SPD GUS CLG SKC L M H  VSB MW MW MW MW AW  ...
029440 99999 190601010600 090   7 *** *** OVC * * *  0.0 ** ** ** ** **  ...
029440 99999 190601011300 ***   0 *** *** OVC * * *  0.0 ** ** ** ** **  ...
029440 99999 190601012000 ***   0 *** *** OVC * * *  0.0 ** ** ** ** **  ...
029440 99999 190601020600 ***   0 *** *** CLR * * *  0.0 ** ** ** ** **  ...

By looking at the data, we can see a few things that we need to consider when reading the data:

The delimiter: The columns are separated with a varying amount of spaces which requires using some special tricks when reading the data with pandas .read_csv() function
NoData values: NaN values in the NOAA data are coded with varying number of * characters and hence we need to be able to instruct pandas to interpret those as NaN.
Many columns: The input data contains many columns (33 in total). Many of those do not contain data we need. Thus, we should probably ignore the unnecessary columns already at this stage.

Handling and cleaning heterogeneous input data (such as in our example here) can be done after reading in the data. However, in many cases it is actually useful to do some cleaning and preprocessing when reading in the data. In fact, it is often much easier to do things this way. For our data file, we can read the data with varying number of spaces between the columns (point 1 above) by using the parameter sep=r"\s+". In this case, we use a raw text string with the sep parameter, which is indicated by the r before the first quotation mark and ensures the escape character \ is handled properly in the sep string. For handling the NoData values (point 2 above), we can tell pandas to consider the * characters as NaN by using the parameter na_values and specifying a list of characters that should be converted to NaN. For this data file we can specify na_values=['*', '**', '***', '****', '*****', '******'], which will then convert the varying number of * characters into NaN values. Finally, we can limit the number of columns that we read (point 3 above) by using the usecols parameter, which we have already used previously. In our case, we are interested in columns that might be somehow useful to our analysis, including the station name, timestamp, and data about temperatures: 'USAF', 'YR--MODAHRMN', 'TEMP', 'MAX', 'MIN'. Achieving all these things is pretty straightforward using the .read_csv() function, as demonstrated below.

import pandas as pd

# Define relative path to the file
fp = "data/029820.txt"

# Read data using varying amount of spaces as separator,
# specifying '*' characters as NoData values,
# and selecting only specific columns from the data
data = pd.read_csv(
    fp,
    sep=r"\s+",
    na_values=["*", "**", "***", "****", "*****", "******"],
    usecols=["USAF", "YR--MODAHRMN", "TEMP", "MAX", "MIN"],
)

Let’s now check the data by printing the first five rows with the .head() function.

data.head()

Perfect, looks good. We have excluded a bunch of unnecessary columns and also the asterisk (*) characters have been correctly converted to NaN values.

Renaming columns#

Let’s take a closer look at the column names of our DataFrame.

print(data.columns)

As we see, some of the column names are a bit unclear with only their names (a description for the columns is available in the metadata data/3505doc.txt). Luckily, it is easy to change labels in a pandas DataFrame using the .rename() function. In order to change the column names, we need to tell pandas how we want to rename the columns using a dictionary that converts the old names to new ones. A dictionary is a specific data structure in Python for storing key-value pairs. We can define the new column names using a dictionary where we list key: value pairs in following manner:

USAF: STATION_ID
YR--MODAHRMN: TIME
TEMP: TEMP_F

Hence, the original column name (e.g., YR--MODAHRMN) is the dictionary key which will be converted to a new column name TIME (which is the value). The temperature values in our data file are again represented in degrees Fahrenheit. We will soon convert these temperatures to degrees Celsius. Thus, in order to avoid confusion with the columns, let’s rename the column TEMP to TEMP_F. Also the station number USAF is much more intuitive if we call it STATION_ID. Below we can create a dictionary for the new column names.

new_names = {
    "USAF": "STATION_ID",
    "YR--MODAHRMN": "TIME",
    "TEMP": "TEMP_F",
}
new_names

Our dictionary format looks correct, so now we can change the column names by passing that dictionary with the parameter columns in the .rename() function.

data = data.rename(columns=new_names)
data.columns

Perfect, now our column names are easier to understand and use.

Using functions with pandas#

Now it’s time to convert those temperatures from degrees Fahrenheit to degrees Celsius. We have done this many times before, but this time we will learn how to apply our own functions to data in a pandas DataFrame. We will define a function for the temperature conversion and then apply this function for each Fahrenheit value on each row of the DataFrame. The output Celsius values will be stored in a new column called TEMP_C. But first, it is a good idea to check some basic properties of our input data before proceeding with data analysis.

# First rows
data.head(2)

# Last rows
data.tail(2)

# Data types
data.info()

Nothing suspicious in the first and last rows, but here the .info() function indicates that the number of observations per column varies if you compare the Non-Null Count information to the number of entries in the data (n = 198334). Only the station number and time seem to have data for each row. All other columns appear to have some missing values. This is not necessarily anything dangerous, but good to keep in mind. Let’s now check the descriptive statistics.

# Descriptive stats
data.describe()

Looking at the TEMP_F values (Fahrenheit temperatures), we can confirm that our measurements seems more or less valid because the value range of the temperatures makes sense (i.e., there are no outliers such as extremely high MAX values or low MIN values). It is always a good practice to critically inspect your data before doing any analysis, as it is possible that your data may include incorrect values (e.g., due to a sensor malfunction or human error).

Defining a function#

Now that we are sure that our data looks correct, and we can start our temperature conversion process by first defining our function to convert from Fahrenheit to Celsius. pandas can use regular functions, so you can simply define functions for pandas exactly the same way as you would do normally (as we learned in Chapter 2, for instance). Let’s define a function that converts from degrees Fahrenheit to Celsius.

def fahr_to_celsius(temp_fahrenheit):
    """Function to convert Fahrenheit temperature into Celsius.

    Parameters
    ----------

    temp_fahrenheit: int | float
        Input temperature in Fahrenheit (should be a number)

    Returns
    -------

    Temperature in Celsius (float)
    """

    # Convert the Fahrenheit into Celsius
    converted_temp = (temp_fahrenheit - 32) / 1.8

    return converted_temp

Now we have the function defined and stored in memory. At this point it is good to test the function with a known value to make sure it works properly.

fahr_to_celsius(32)

32 degrees Fahrenheit is indeed 0 degrees Celsius, so our function seem to be working correctly.

Using a function by iterating over rows#

Next we will learn how to use our function with data stored in a pandas DataFrame. We will first apply the function row by row using a for loop and then we will learn a more efficient way of applying the function to all rows at once.

Looping over rows in a DataFrame can be done in a few different ways. A common approach is to use the .iterrows() method which loops over the rows as index-Series pairs. In other words, we can use the .iterrows() method together with a for loop to repeat a process for each row in a pandas DataFrame. Please note that iterating over rows this way is a rather inefficient approach, but it is still useful to understand the logic behind how this works. When using the .iterrows() method it is important to understand that .iterrows() accesses not only the values of one row, but also the index of the row. Let’s start with a simple example for loop that goes through each row in our DataFrame.

# Iterate over the rows
for idx, row in data.iterrows():
    # Print the index value
    print("Index:", idx)

    # Print the temperature from the row
    print("Temp F:", row["TEMP_F"], "\n")

    break

We can see that the idx variable indeed contains the index value 0 (the first row) and the row variable contains all the data from that row stored as a pandas Series. Also, notice that when developing a for loop you do not always need to iterate through the entire loop if you just want to test things out. Using the break statement in Python terminates a loop wherever it is placed inside the loop. In our case we used it to check out the values on the first row of the DataFrame. This saves time and allows us to test the code logic without printing thousands of values to the screen!

Next, let’s create an empty column TEMP_C for the Celsius temperatures and update the values in that column using the fahr_to_celsius() function that we defined earlier. For updating the value in the DataFrame, we can use the .at[] indexer that we used earlier in this chapter. This time, however, we will use the .itertuples() method to access the rows in the DataFrame. The .itertuples() method works similarly to .iterrows(), except it returns only the row values without the index. In addition, the returned values are not a pandas Series, but instead .itertuples() returns a named tuple data type. As a result, when using .itertuples() accessing the row values needs to be done a bit differently. Remember, a tuple is like a list but immutable and a “named tuple” is a special kind of tuple object that adds the ability to access the values by name instead of position index. Thus, we can access the TEMP_F value in a given row using row.TEMP_F (in contrast to how we accessed the value in the example above). We will not work with named tuples in the rest of the book, but more information can be found in the Python documentation for named tuples [2].

Let’s see an example of how to use the .itertuples() method.

# Create an empty column for the output values
data["TEMP_C"] = 0.0

# Iterate over the rows
for row in data.itertuples():
    # Convert the Fahrenheit to Celsius
    # Notice how we access the row value
    celsius = fahr_to_celsius(row.TEMP_F)

    # Update the value for 'Celsius' column with the converted value
    # Notice how we can access the Index value
    data.at[row.Index, "TEMP_C"] = celsius

# Check the result
data.head()

# What does our row look like?
row._asdict()

Okay, now we have iterated over our data, converted the temperatures to degrees Celsius using our fahr_to_celsius() function, and stored the results in the TEMP_C column. The values look correct as 32 degrees Fahrenheit indeed is 0 degrees Celsius, which can be seen on the second row. We also have the last row of our DataFrame stored in the variable row from the code above, which is a named tuple that has been converted to the dictionary data type using the ._asdict() method for named tuples.

Before moving to other more efficient ways to use functions with a pandas DataFrame, we should note a few things about the approaches above. We demonstrated use of the .itertuples() method for looping over the values because it is significantly faster than .iterrows() (can be around 100 times faster). We also used .at[] to assign the value in the DataFrame because it is designed to access single values more efficiently than the .loc[] indexer, which can access groups of rows and columns. That said, you could have also simply used data.loc[idx, new_column] = celsius to achieve the same result as both examples above. It is simply slower.

Using a function with the apply method#

Although using a for loop with .itertuples() can be fairly efficient, the pandas DataFrame and Series data structures have a dedicated method called .apply() for applying functions in columns (or rows). .apply() is typically faster than .itertuples(), especially if you have large number of rows like in our case. When using .apply(), we pass the function that we want to use as an argument. Let’s start by applying our fahr_to_celsius() function to the TEMP_F column with the temperature values in Fahrenheit.

data["TEMP_F"].apply(fahr_to_celsius)

The results again look logical. Notice how we passed the fahr_to_celsius() function without using the parentheses () after the name of the function. When using .apply(), you should always leave out the parentheses from the function that you use. In other words, you should use .apply(fahr_to_celsius) not .apply(fahr_to_celsius()). Why? Because the .apply() method will execute and use the function itself in the background when it operates with the data. If we would pass our function including the parentheses, the fahr_to_celsius() function would actually be executed once before the loop with .apply() starts (thus becoming unusable) and that is not what we want.

Our previous command only returned the Series of temperatures to the screen, but naturally we can also store them permanently in a column of our DataFrame (overwriting the old values).

data["TEMP_C"] = data["TEMP_F"].apply(fahr_to_celsius)

A nice thing with .apply() is that we can also apply the function on several columns at once. Below, we also sort the values in descending order based on values in the MIN column to confirm that applying our function really works.

cols = ["TEMP_F", "MIN", "MAX"]
result = data[cols].apply(fahr_to_celsius)
result.sort_values(by="MIN", ascending=False).head()

You can also directly store the outputs to the DataFrame columns TEMP_C, MIN_C, and MAX_C.

cols = ["TEMP_F", "MIN", "MAX"]
newcols = ["TEMP_C", "MIN_C", "MAX_C"]
data[newcols] = data[cols].apply(fahr_to_celsius)
data.head()

In this section, we showed you a few different ways to iterate over rows in pandas and apply functions. The most important thing is that you understand the logic of how loops work and how you can use your own functions to modify the values in a pandas DataFrame. Whenever you need to loop over your data, we recommend using .apply() as it is typically the most efficient one in terms of execution time. Remember that in most cases you do not need to use loops. Instead you can do calculations in a “vectorized manner” (which is the fastest way) as we learned previously when doing basic calculations in pandas.

String slicing#

We will eventually want to group our data based on month in order to see if the January temperatures in 2020 were higher than on average (which is the goal in our analysis as you might recall). Currently, the date and time information is stored in the column TIME that has a structure yyyyMMddhhmm. This is a typical timestamp format in which yyyy equals to year in four digit format, MM to month (two digits), dd days, hh hours and mm minutes. Let’s have a closer look at the date and time information we have by checking the values in that column, and their data type:

data["TIME"].head()

data["TIME"].tail()

The TIME column contains several observations per day (and even several observations per hour). The timestamp for the first observation is 190601010600, i.e. from 1st of January 1906 (way back!), and the timestamp for the latest observation is 201910012350. As we can see, the data type (dtype) of our column seems to be int64, i.e. the information is stored as integer values.

We want to aggregate this data on a monthly level. In order to do so, we need to “label” each row of data based on the month when the record was observed. Hence, we need to somehow separate information about the year and month for each row. In practice, we can create a new column (or an index) containing information about the month (including the year, but excluding days, hours and minutes). There are different ways of achieving this, but here we will take advantage of string slicing which means that we convert the date and time information into character strings and “cut” the needed information from the string objects. The other option would be to convert the timestamp values into something called datetime objects, but we will learn about those a bit later. Before further processing, we first want to convert the TIME column as character strings for convenience, stored into a new column TIME_STR:

data["TIME_STR"] = data["TIME"].astype(str)

If we look at the latest time stamp in the data (201910012350), you can see that there is a systematic pattern YEAR-MONTH-DAY-HOUR-MINUTE. Four first characters represent the year, and the following two characters represent month. Because we are interested in understanding monthly averages for different years, we want to slice the year and month values from the timestamp (the first 6 characters), like this:

date = "201910012350"
date[0:6]

Based on this information, we can slice the correct range of characters from the TIME_STR column using a specific pandas function designed for Series, called .str.slice(). As parameters, the function has start and stop which you can use to specify the positions where the slicing should start and end:

data["YEAR_MONTH"] = data["TIME_STR"].str.slice(start=0, stop=6)
data.head()

Nice! Now we have “labeled” the rows based on information about day of the year and hour of the day.

Question 3.5#

Create a new column 'MONTH' with information about the month without the year.

Grouping and aggregating data#

Basic logic of grouping a DataFrame using `.groupby()`#

In the following sections, we want to calculate the average temperature for each month in our dataset. Here, we will learn how to use a .groupby() method which is a handy tool for compressing large amounts of data and computing statistics for subgroups. We will use the groupby method to calculate the average temperatures for each month trough these three main steps:

group the data based on year and month using groupby()
calculate the average temperature for each month (i.e. each group)
store the resulting rows into a DataFrame called monthly_data

We have quite a few rows of weather data (N=198334), and several observations per day. Our goal is to create an aggreated DataFrame that would have only one row per month. The .groupby() takes as a parameter the name of the column (or a list of columns) that you want to use as basis for doing the grouping. Let’s start by grouping our data based on unique year and month combination:

grouped = data.groupby("YEAR_MONTH")

Notice, thas it would also be possible to create combinations of years and months “on-the-fly” if you have them in separate columns. In such case, grouping the data could be done as grouped = data.groupby(['YEAR', 'MONTH']). Let’s explore the new variable grouped:

print(type(grouped))
print(len(grouped))

We have a new object with type DataFrameGroupBy with 826 groups. In order to understand what just happened, let’s also check the number of unique year and month combinations in our data:

data["YEAR_MONTH"].nunique()

Length of the grouped object should be the same as the number of unique values in the column we used for grouping (YEAR_MONTH). For each unique value, there is a group of data. Let’s explore our grouped data further by check the “names” of the groups (five first ones). Here, we access the keys of the groups and convert them to a list so that we can slice and print only a few of those to the sceen:

list(grouped.groups.keys())[:5]

Let’s check the contents for a group representing January 1906. We can get the values for that month from the grouped object using the get_group() method:

# Specify a month (as character string)
month = "190601"

# Select the group
group1 = grouped.get_group(month)
group1

As we can see, a single group contains a DataFrame with values only for that specific month. Let’s check the DataType of this group:

type(group1)

So, one group is a pandas DataFrame which is really useful, because it allows us to use all the familiar DataFrame methods for calculating statistics etc. for this specific group which we will see shortly. It is also possible to iterate over the groups in our DataFrameGroupBy object which can be useful if you need to conduct and apply some more complicated subtasks for each group. When doing so, it is important to understand that a single group in our DataFrameGroupBy actually contains not only the actual values, but also information about the key that was used to do the grouping. Hence, when iterating we need to assign the key and the values (i.e. the group) into separate variables. Let’s see how we can iterate over the groups and print the key and the data from a single group (again using break to only see what is happening):

# Iterate over groups
for key, group in grouped:
    # Print key and group
    print("Key:\n", key)
    print("\nFirst rows of data in this group:\n", group.head())

    # Stop iteration with break command
    break

Here, we can see that the key contains the name of the group (i.e. the unique value from YEAR_MONTH).

Aggregating data with `groupby()`#

We can, for example, calculate the average values for all variables using the statistical functions that we have seen already (e.g. mean, std, min, max, median). To calculate the average temperature for each month, we can use the mean() function. Let’s calculate the mean for all the weather related data attributes in our group at once:

# Specify the columns that will be part of the calculation
mean_cols = ["TEMP_F", "TEMP_C"]

# Calculate the mean values all at one go
mean_values = group1[mean_cols].mean()
mean_values

As a result, we get a pandas Series with mean values calculated for all columns in the group. Notice that if you want to convert this Series back into a DataFrame (which can be useful if you e.g. want to merge multiple groups), you can use command .to_frame().T which first converts the Series into a DataFrame and then transposes the order of the axes (the label names becomes the column names):

# Convert to DataFrame
mean_values.to_frame().T

To do a similar aggregation with all the groups in our data, we can actually combine the groupby() function with the aggregation step (such as taking the mean, median etc. of given columns), and finally restructure the resulting DataFrame a bit. This can be at first a bit harder to understand, but this is how you would do the grouping and aggregating the values as follows:

# The columns that we want to aggregate
mean_cols = ["TEMP_F", "TEMP_C"]

# Group and aggregate the data with one line
monthly_data = data.groupby("YEAR_MONTH")[mean_cols].mean().reset_index()
monthly_data

As we can see, aggregating the data in this way is fairly straightforward and fast process requiring merely a single command. So what did we actually do here? We i) grouped the data, ii) selected specific columns from the result (mean_cols), iii) calculated the mean for all of the selected columns of the groups, and finally 4) reset the index. Resetting the index at the end is not necessary, but by doing it, we turn the YEAR_MONTH values into a dedicated column in our data (which would be otherwise store as index) .

What might not be obvious from this example is the fact that hidden in the background, each group is actually iterated over and the aggregation step is repeated for each group. For you to better understand what happens, we will next repeat the same process by iterating over groups and eventually creating a DataFrame that will contain the mean values for all those weather attributes that we were interested in. In this approach, we will first iterate over the groups, then calculate the mean values, store the result into a list, and finally merge the aggregated data into a DataFrame called monthly_data.

# Create an empty list for storing the aggregated rows/DataFrames
data_container = []

# The columns that we want to aggregate
mean_cols = ["TEMP_F", "TEMP_C"]

# Iterate over the groups
for key, group in grouped:
    # Calculate mean
    mean_values = group[mean_cols].mean()

    # Add the ´key´ (i.e. the date+time information) into the Series
    mean_values["YEAR_MONTH"] = key

    # Convert the pd.Series into DataFrame and
    # append the aggregated values into a list as a DataFrame
    data_container.append(mean_values.to_frame().T)

# After iterating all groups, merge the list of DataFrames
monthly_data = pd.concat(data_container)
monthly_data

As a result, we get identical results as with the earlier approach that was done with a single line of code (except for the position of the YEAR_MONTH column).

So which approach should you use? From the performance point of view, we recommend using the first approach (i.e. chaining) which does not require you to create a separate for loop, and is highly performant. However, this approach might be a bit more difficult to read and comprehend (the loop might be easier). Also sometimes you want to include additional processing steps inside the loop which can be hard accomplish by chaining everything into a single command. Hence, it is useful to know both of these approaches for doing aggregations with the data.

Case study: Detecting warm months#

Now, we have aggregated our data on monthly level and all we need to do is to check which years had the warmest January temperatures. A simple approach is to select all January values from the data and check which group(s) have the highest mean value. Before doing this, let’s separate the month information from our timestamp following the same approach as previously we did when slicing the year-month combination:

monthly_data["MONTH"] = monthly_data["YEAR_MONTH"].str.slice(start=4, stop=6)
monthly_data.head()

Now we can select the values for January from our data and store it into a new variable january_data. We will also check the highest temperature values by sorting the DataFrame in a descending order:

january_data = monthly_data.loc[monthly_data["MONTH"] == "01"]
january_data.sort_values(by="TEMP_C", ascending=False).head()

By looking at the order of YEAR_MONTH column, we can see that January 2020 indeed was on average the warmest month on record based on weather observations from Finland.

Automating the analysis#

Now we have learned how to aggregate data using pandas. average temperatures for each month based on hourly weather observations. One of the most useful aspects of programming, is the ability to automate processes and repeat analyses such as these for any number of weather stations (assuming the data structure is the same).

Hence, let’s now see how we can repeat the previous data analysis steps for 15 weather stations located in different parts of Finland containing data for five years (2015-2019). The idea is that we will repeat the process for each input file using a (rather long) for loop. We will use the most efficient alternatives of the previously represented approaches, and finally will store the results in a single DataFrame for all stations. We will learn how to manipulate filepaths in Python using the pathlib module and see how we can list our input files in the data directory data/finnish_stations. We will store those paths to a variable file_list, so that we can use the file paths easily in the later steps.

Managing and listing filesystem paths#

In Python there are two commonly used approaches to manage and manipulate filepaths, namely os.path sub-module and a newer pathlib module (available since Python 3.4) which we will demonstrate here. The built-in module pathlib provides many useful functions for interacting and manipulating filepaths on your operating system. On the following, we have data in different sub-folders and we will learn how to use the Path class from the pathlib library to construct filepaths. Next, we will import and use the Path class and see how we can construct a filepath by joining a folder path and file name:

from pathlib import Path

# Initialize the Path
input_folder = Path("data/finnish_stations")

# Join folder path and filename
fp = input_folder / "028360.txt"
fp

Here, we first initialized the Path object and stored it in variable input_folder by passing a relative path as a string to directory where all our files are located. Then we created a full filepath to file 028360.txt by adding a forward slash (/) character between the folder and the filename which joins them together (easy!). In this case, our end result is something called a PosixPath which is a filesystem path to a given file on Linux or Mac operating systems. If you would run the same commands on Windows machine, the end result would be a WindowsPath. Hence, the output depends on which operating system you are using. However, you do not need to worry about this, because both types of Paths work exactly the same, no matter which operating system you use.

Both the Path object that we stored in input_folder variable and the PosixPath object that we stored in variable fp are actually quite versatile creatures, and we can do many useful things with them. For instance, we can find the parent folder where the file is located, extract the filename from the full path, test whether the file or directory actually exists, find various properties of the file (such as size of the file or creation time), and so on:

fp.parent

fp.name

fp.exists()

# File properties
size_in_bytes = fp.stat().st_size
creation_time = fp.stat().st_ctime
modified_time = fp.stat().st_mtime
print(
    f"Size (bytes): {size_in_bytes}\nCreated (seconds since Epoch): {creation_time}\nModified (seconds since Epoch): {modified_time}"
)

There are also various other methods that you can do with pathlib, such as rename the files (.rename()) or create folders (.mkdir()). You can see all available methods from pathlib documentation [3]. One of the most useful tools in pathlib is the ability to list all files within a given folder by using the method .glob() which also allows you to add specific search criteria for listing only specific files from the directory:

file_list = list(input_folder.glob("0*txt"))

Here, the result is stored into variable file_list as a list. By default, the .glob() produces something called a generator which is a “lazy iterator”, i.e. a special kind of function that allows you to iterate over items like a list, but without actually storing the data in memory. By enclosing the .glob() search functionality with list() we convert this generator into a normal Python list. Note that we’re using the * character as a wildcard, so any filename that starts with 0 and ends with txt will be added to the list of files. We specifically use the number 0 as the starting part for the search criteria to avoid having metadata files included in the list. Let’s take a look what we got as a result:

print("Number of files in the list:", len(file_list))
file_list

Iterate over input files and repeat the analysis#

Now, we should have all the relevant file paths in the file_list, and we can loop over the list using a for loop (again we break the loop after first iteration):

for fp in file_list:
    print(fp)
    break

The data that we have sampled is in regular CSV format which we can read easily with pd.read_csv() function:

data = pd.read_csv(fp)
data.head()

Now we have all the file paths to our weather observation datasets in a list, and we can start iterating over them and repeat the analysis steps for each file separately. We keep all the analytical steps inside a loop so that all of them are repeated to different stations. Finally, we will store the warmest January for each station in a list called results using a regular Python’s append() method and merge the list of DataFrames into one by using pd.concat() function:

# A list for storing the result
results = []

# Repeat the analysis steps for each input file:
for fp in file_list:
    # Read the data from CSV file
    data = pd.read_csv(fp)

    # Rename the columns
    new_names = {
        "USAF": "STATION_NUMBER",
        "YR--MODAHRMN": "TIME",
        "TEMP": "TEMP_F",
    }
    data = data.rename(columns=new_names)

    # Print info about the current input file
    # This is useful to understand how the process proceeds
    print(
        f"STATION NUMBER: {data.at[0,'STATION_NUMBER']}\tNUMBER OF OBSERVATIONS: {len(data)}"
    )

    # Create column
    col_name = "TEMP_C"
    data[col_name] = None

    # Convert temperatures from Fahrenheit to Celsius
    data["TEMP_C"] = data["TEMP_F"].apply(fahr_to_celsius)

    # Convert TIME to string
    data["TIME_STR"] = data["TIME"].astype(str)

    # Parse year and month and convert them to numbers
    data["MONTH"] = data["TIME_STR"].str.slice(start=5, stop=6).astype(int)
    data["YEAR"] = data["TIME_STR"].str.slice(start=0, stop=4).astype(int)

    # Extract observations for the months of January
    january = data[data["MONTH"] == 1]

    # Aggregate the data and get mean values
    columns = ["TEMP_F", "TEMP_C", "STATION_NUMBER"]
    monthly_mean = january.groupby(by=["YEAR", "MONTH"])[columns].mean().reset_index()

    # Sort the values and take the warmest January
    warmest = monthly_mean.sort_values(by="TEMP_C", ascending=False).head(1)

    # Add to results
    results.append(warmest)

# Merge all the results into a single DataFrame
results = pd.concat(results)

Awesome! Now we have conducted the same analysis for 15 weather stations in Finland and it did not took too many lines of code! We were able to follow how the process advances with the printed lines of information, i.e. we did some simple logging of the operations. Let’s finally investigate our results:

results

Each row in the results represents the warmest January at given STATION_NUMBER between the years 2015 and 2019. Based on the YEAR column, the warmest January in most of Finland’s weather stations during this five-year period was in 2015. We can confirm this by checking the value counts of the YEAR column:

results["YEAR"].value_counts()

Data wrangling, grouping and aggregation

Contents

Data wrangling, grouping and aggregation#

Cleaning data while reading#

Renaming columns#

Using functions with pandas#

Defining a function#

Using a function by iterating over rows#

Using a function with the apply method#

String slicing#

Question 3.5#

Grouping and aggregating data#

Basic logic of grouping a DataFrame using `.groupby()`#

Aggregating data with `groupby()`#

Case study: Detecting warm months#

Automating the analysis#

Managing and listing filesystem paths#

Iterate over input files and repeat the analysis#

Footnotes#

Data wrangling, grouping and aggregation

Contents

Data wrangling, grouping and aggregation#

Cleaning data while reading#

Renaming columns#

Using functions with pandas#

Defining a function#

Using a function by iterating over rows#

Using a function with the apply method#

String slicing#

Question 3.5#

Grouping and aggregating data#

Basic logic of grouping a DataFrame using .groupby()#

Aggregating data with groupby()#

Case study: Detecting warm months#

Automating the analysis#

Managing and listing filesystem paths#

Iterate over input files and repeat the analysis#

Footnotes#

Basic logic of grouping a DataFrame using `.groupby()`#

Aggregating data with `groupby()`#