Ex. 2.3.4 (Exercise 2)

[AkselSB]

Hi, I'm having issues updating the annotated function in 2.1.1 with the functions processing "area" and "temporal data."

The following code runs with no errors, but the new functions are not included: `# INCLUDED IN ASSIGNMENT 1# YOUR CODE HERE import pandas as pd

importing the tool

def load_weather(year):

url = f"ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/{year}.csv.gz" 

df_weather = pd.read_csv(url, 
                         header=None)  
df_weather = df_weather.iloc[:,:4] 

column_names = ['station', 'datetime', 'obs_type', 'obs_value'] 
df_weather.columns = column_names

df_weather['obs_value'] = df_weather['obs_value'] / 10 

selection_tmax = df_weather.obs_type == 'TMAX'
df_select = df_weather.loc[selection_tmax] 

df_weather['Area Code'] = df_weather['station'].str[:3] #<<<Area function

df_weather['datetime'] = pd.to_datetime(df_weather['datetime'], format='%Y%m%d') #<<<datetime function
df_weather = df_weather.rename(columns={'datetime':'datetime_dt'})

df_sorted = df_select.sort_values(by=['station', 'datetime']) 

df_reset = df_sorted.reset_index(drop=True)     
df_out = df_reset.copy()

return df_out

load_weather(1863)`

[MatPiq]

Hi Aksel! You are using a lot of variable names, somewhere in your code this probably leads to the changes to the df not being assigned to the variable you intended. This is actually a good example of one of the benefits with the method-chaining approach :) /Matias

[AkselSB]

I've tried to chain the functions previously, but cannot figure out where the new functions would fit in when using this approach:

`def load_weather(year):

url = f"ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/{year}.csv.gz" 

df_weather = pd.read_csv(url, 
                         header=None)  #the function uses pandas to read the csv.file      

df_weather = df_weather.iloc[:,:4] 

column_names = ['station', 'datetime', 'obs_type', 'obs_value'] 
df_weather.columns = column_names 

df_weather['obs_value'] = df_weather['obs_value'] / 10 

df_out = df_weather\
    .loc[df_weather.obs_type == 'TMAX']\
    .sort_values(by=['station', 'datetime'])\
    .reset_index(drop=True)\
    .copy()

return df_out 

load_weather(1863)`

[MatPiq]

You don't necessarily need to use the method-chaining approach if you were able to now make it work, but if you are curious and want to try doing everything in one chain I would recommend reading up on the method ".assign()" https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.assign.html. With this you can create new variables/columns within the chain.