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filter out empty os versions #8

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Merged
AndreWohnsland merged 1 commit into from
Apr 21, 2025
Merged

filter out empty os versions #8

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27 changes: 15 additions & 12 deletions frontend/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def get_installations():
logger.warning(
"Error from backend: %s: %s",
installations_response.status_code,
installations_response.text
installations_response.text,
)
except (ConnectTimeout, ReadTimeout, rConnectionError):
logger.error("Timeout when connecting to backend.")
Expand All @@ -89,6 +89,8 @@ def get_installations():
df[InstallationSchema.OS] = df[InstallationSchema.OS].str.replace(r"(Raspbian |Debian )", "Debian ", regex=True)
# convert all entries of os having "Armbian" in the name to "Armbian"
df[InstallationSchema.OS] = df[InstallationSchema.OS].str.replace(r"Armbian.*", "Armbian (all)", regex=True)
# it might be that there is an empty string for the os, we need to remove those
df = df[df[InstallationSchema.OS] != ""]
return df


Expand All @@ -99,7 +101,7 @@ def filter_dataframe(
machines: list,
recipes: list,
only_one_day: bool,
dates: tuple[datetime.date, datetime.date]
dates: tuple[datetime.date, datetime.date],
):
"""Apply the sidebar filter option to the data."""
filtered_df = df.loc[
Expand All @@ -110,8 +112,7 @@ def filter_dataframe(
& (df[CocktailSchema.receivedate] <= pd.Timestamp(dates[1]) + pd.Timedelta(days=1))
]
if only_one_day:
filtering = filtered_df[CocktailSchema.receivedate] >= (
datetime.datetime.now() - datetime.timedelta(hours=24)) # type: ignore
filtering = filtered_df[CocktailSchema.receivedate] >= (datetime.datetime.now() - datetime.timedelta(hours=24)) # type: ignore
filtered_df = filtered_df[filtering]
return filtered_df

Expand Down Expand Up @@ -161,8 +162,12 @@ def cocktail_count(df: pd.DataFrame, limit_recipe: int, country_split: bool) ->
return cocktails.iloc[:limit_recipe]
# If split by country, for the listing, we need to generate a tmp rank
# that we can order by that rank for the cocktail name (its dependant on total count)
name_order = df.groupby([CocktailSchema.cocktail_name])[
CocktailSchema.volume].count().sort_values().index.to_list()[-limit_recipe:]
name_order = (
df.groupby([CocktailSchema.cocktail_name])[CocktailSchema.volume]
.count()
.sort_values()
.index.to_list()[-limit_recipe:]
)
sorter_index = dict(zip(name_order, range(len(name_order))))
cocktails["Rank"] = cocktails[CocktailSchema.cocktail_name].map(sorter_index)
cocktails.sort_values(["Rank", CocktailSchema.cocktail_count], ascending=False, inplace=True)
Expand Down Expand Up @@ -223,8 +228,7 @@ def serving_aggregation(df: pd.DataFrame, machine_split: bool, min_count: int):
@st.cache_data(ttl=300)
def aggregate_installations(df: pd.DataFrame):
return (
df
.groupby([InstallationSchema.OS])[InstallationSchema.RECEIVEDATE]
df.groupby([InstallationSchema.OS])[InstallationSchema.RECEIVEDATE]
.count()
.reset_index()
.rename(
Expand All @@ -241,13 +245,12 @@ def cumulate_installations(raw_df: pd.DataFrame, os_split: bool = False):
"""Group the installations by week and returns the count."""
df = raw_df.copy(deep=True)
df["counter"] = 1
grouping = [pd.Grouper(key=InstallationSchema.RECEIVEDATE, freq='w')]
grouping = [pd.Grouper(key=InstallationSchema.RECEIVEDATE, freq="w")]
# also need to group by os if needed
if os_split:
grouping.insert(0, InstallationSchema.OS) # type: ignore
df = (
df
.groupby(grouping)
df.groupby(grouping)
.count()
.reset_index()
.rename(
Expand Down Expand Up @@ -275,7 +278,7 @@ def cumulate_installations(raw_df: pd.DataFrame, os_split: bool = False):
df,
index=InstallationSchema.RECEIVEDATE,
columns=InstallationSchema.OS,
values=InstallationSchema.INSTALLATIONS_COUNT
values=InstallationSchema.INSTALLATIONS_COUNT,
)
.sort_index()
.ffill()
Expand Down