Extract method to reuse.
junos
parent
c688580fe8
commit
82b53bc0d3
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@ -347,3 +347,69 @@ def increment_answers(df_esm_clean: pd.DataFrame, increment_by=1):
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print("Please, clean the dataframe first using features.esm.clean_up_esm.")
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print("Please, clean the dataframe first using features.esm.clean_up_esm.")
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print(e)
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print(e)
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return df_esm_clean
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return df_esm_clean
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def reassign_question_ids(
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df_esm_cleaned: pd.DataFrame, question_ids_content: dict
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) -> pd.DataFrame:
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"""
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Fix question IDs to match their actual content.
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Unfortunately, when altering the protocol to adapt to COVID pandemic,
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we did not retain original question IDs.
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This means that for participants before 2021, they are different
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from for the rest of them.
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This function searches for question IDs by matching their strings.
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Parameters
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----------
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df_esm_cleaned: pd.DataFrame
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A cleaned up dataframe, which must also include esm_user_answer_numeric.
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question_ids_content: dict
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A dictionary, linking question IDs with their content ("instructions").
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Returns
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-------
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df_esm_fixed: pd.DataFrame
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The same dataframe but with fixed question IDs.
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"""
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df_esm_unique_questions = (
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df_esm_cleaned.groupby("question_id")
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.esm_instructions.value_counts()
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.rename()
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.reset_index()
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)
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# Tabulate all possible answers to each question (group by question ID).
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# First, check that we anticipated all esm instructions.
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for q_id in question_ids_content.keys():
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# Look for all questions ("instructions") occurring in the dataframe.
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actual_questions = df_esm_unique_questions.loc[
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df_esm_unique_questions["question_id"] == q_id,
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"esm_instructions",
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]
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# These are all answers to a given question (by q_id).
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questions_matches = actual_questions.str.startswith(
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question_ids_content.get(q_id)
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)
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# See if they are expected, i.e. included in the dictionary.
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if ~actual_questions.all():
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print("One of the questions that occur in the data was undefined.")
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print("This were the questions found in the data: ")
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raise KeyError(actual_questions[~questions_matches])
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# In case there is an unexpected answer, raise an exception.
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# Next, replace question IDs.
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df_esm_fixed = df_esm_cleaned.copy()
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df_esm_fixed["question_id"] = df_esm_cleaned["esm_instructions"].apply(
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lambda x: next(
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(
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key
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for key, values in question_ids_content.items()
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if x.startswith(values)
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),
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None,
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)
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)
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return df_esm_fixed
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@ -1,5 +1,3 @@
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import pandas as pd
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COPE_ORIGINAL_MAX = 4
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COPE_ORIGINAL_MAX = 4
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COPE_ORIGINAL_MIN = 1
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COPE_ORIGINAL_MIN = 1
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@ -125,65 +123,3 @@ DICT_COPE_QUESTION_IDS = {
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"Razburil sem se in razmišljal samo o tem",
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"Razburil sem se in razmišljal samo o tem",
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),
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),
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}
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}
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def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
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"""
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Fix question IDs to match their actual content.
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Unfortunately, when altering the protocol to adapt to COVID pandemic,
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we did not retain original question IDs.
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This means that for participants before 2021, they are different
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from for the rest of them.
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This function searches for question IDs by matching their strings.
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Parameters
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----------
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df_cope_cleaned: pd.DataFrame
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A cleaned up dataframe, which must also include esm_user_answer_numeric.
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Returns
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-------
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df_cope_fixed: pd.DataFrame
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The same dataframe but with fixed question IDs.
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"""
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df_esm_cope_unique_questions = (
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df_cope_cleaned.groupby("question_id")
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.esm_instructions.value_counts()
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.rename()
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.reset_index()
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)
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# Tabulate all possible answers to each question (group by question ID).
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# First, check that we anticipated all esm instructions.
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for q_id in DICT_COPE_QUESTION_IDS.keys():
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# Look for all questions ("instructions") occurring in the dataframe.
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actual_questions = df_esm_cope_unique_questions.loc[
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df_esm_cope_unique_questions["question_id"] == q_id,
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"esm_instructions",
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]
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# These are all answers to a given question (by q_id).
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questions_matches = actual_questions.str.startswith(
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DICT_COPE_QUESTION_IDS.get(q_id)
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)
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# See if they are expected, i.e. included in the dictionary.
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if ~actual_questions.all():
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print("One of the questions that occur in the data was undefined.")
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print("This were the questions found in the data: ")
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raise KeyError(actual_questions[~questions_matches])
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# In case there is an unexpected answer, raise an exception.
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# Next, replace question IDs.
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df_cope_fixed = df_cope_cleaned.copy()
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df_cope_fixed["question_id"] = df_cope_cleaned["esm_instructions"].apply(
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lambda x: next(
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(
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key
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for key, values in DICT_COPE_QUESTION_IDS.items()
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if x.startswith(values)
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),
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None,
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)
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)
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return df_cope_fixed
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@ -444,65 +444,3 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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# TODO: How many questions about the stressfulness of the period were asked
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# TODO: How many questions about the stressfulness of the period were asked
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# and how does this relate to events?
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# and how does this relate to events?
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def reassign_question_ids(df_sam_cleaned: pd.DataFrame) -> pd.DataFrame:
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"""
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Fix question IDs to match their actual content.
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Unfortunately, when altering the protocol to adapt to COVID pandemic,
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we did not retain original question IDs.
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This means that for participants before 2021, they are different
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from for the rest of them.
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This function searches for question IDs by matching their strings.
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Parameters
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----------
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df_sam_cleaned: pd.DataFrame
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A cleaned up dataframe, which must also include esm_user_answer_numeric.
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Returns
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-------
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df_sam_fixed: pd.DataFrame
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The same dataframe but with fixed question IDs.
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"""
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df_esm_sam_unique_questions = (
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df_sam_cleaned.groupby("question_id")
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.esm_instructions.value_counts()
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.rename()
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.reset_index()
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)
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# Tabulate all possible answers to each question (group by question ID).
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# First, check that we anticipated all esm instructions.
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for q_id in DICT_SAM_QUESTION_IDS.keys():
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# Look for all questions ("instructions") occurring in the dataframe.
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actual_questions = df_esm_sam_unique_questions.loc[
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df_esm_sam_unique_questions["question_id"] == q_id,
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"esm_instructions",
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]
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# These are all answers to a given question (by q_id).
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questions_matches = actual_questions.str.startswith(
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DICT_SAM_QUESTION_IDS.get(q_id)
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)
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# See if they are expected, i.e. included in the dictionary.
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if ~actual_questions.all():
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print("One of the questions that occur in the data was undefined.")
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print("This were the questions found in the data: ")
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raise KeyError(actual_questions[~questions_matches])
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# In case there is an unexpected answer, raise an exception.
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# Next, replace question IDs.
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df_sam_fixed = df_sam_cleaned.copy()
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df_sam_fixed["question_id"] = df_sam_cleaned["esm_instructions"].apply(
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lambda x: next(
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(
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key
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for key, values in DICT_SAM_QUESTION_IDS.items()
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if x.startswith(values)
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),
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None,
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)
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)
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return df_sam_fixed
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