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5/en/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 45b5230784e5d2f786047b47b9f6f233
+tags: 645f666f9bcd5a90fca523b33c5a78b7

5/en/_downloads/0a85bf6b6f5c0377014b2035ca60c31e/scientific-computing.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Scientific Computing and Visualization with Spyder
+
+Created on Thu May 20 10:17:27 2021
+
+@author: Spyder Team
+"""
+
+# %% Import libraries
+import matplotlib.pyplot as plt
+import pandas as pd
+import scipy.stats as stats
+import seaborn as sns
+
+
+# %% Load raw data (parquet)
+data = pd.read_parquet("parsed_data_public.parquet")
+
+
+# %% Let's explore age
+print(data.d_age.describe())
+
+age = data.d_age.tolist()
+
+
+# %% Save some variables and display them in the Variable Explorer
+max_age = data.d_age.min()
+min_age = data.d_age.max()
+
+
+# %% Plot age with pandas
+data.d_age.plot.hist(bins=25, alpha=0.5)
+
+
+# %% Plot age with seaborn (and search for help from IPython Console)
+sns.histplot(data.d_age, kde=True, bins=25)
+plt.show()
+
+
+# %% Plot age and mean
+sns.histplot(data.d_age, kde=True, bins=25)
+plt.xlabel('Age')
+plt.axvline(data.d_age.mean(), color='k', linestyle='dashed', linewidth=1)
+min_ylim, max_ylim = plt.ylim()
+plt.text(data.d_age.mean()*1.1, max_ylim*0.9,
+ 'Mean: {:.2f}'.format(data.d_age.mean()))
+plt.show()
+
+
+# %% Demographic variables list
+demograph = [v for v in list(data.columns) if v.startswith("d_")]
+
+
+# %% Cognitive ability questions
+# Select the questions for the cognitive ability test (14 questions).
+# Add the correct answers in a new column.
+
+test_items = pd.read_csv("test_items.csv")
+
+ca_test = data.copy() # Make a copy of the original dataframe
+
+right_answers = []
+for ID, ROW in test_items.iterrows():
+ right_answers.append(ROW.iloc[ROW["option_correct"] + 2])
+test_items["right_answer"] = right_answers
+
+
+for ID, ROW in test_items.iterrows():
+ QUESTION = "q" + str(ROW["ID"])
+ ANSWER = str(ROW["right_answer"])
+ try:
+ ca_test.dropna(subset=[QUESTION], inplace=True)
+ ca_test["resp_" + QUESTION] = ca_test.apply(lambda row: row[QUESTION] == ANSWER, axis=1)
+ except KeyError:
+ print(f"{QUESTION} not found.")
+
+
+# The identification of some answers failed due to formal discrepancies.
+ca_test.q18154 = pd.Series(ca_test.q18154, dtype="int")
+ca_test.q18154 = pd.Series(ca_test.q18154, dtype="string")
+ca_test.resp_q18154 = ca_test.apply(lambda row: row["q18154"] == "26", axis=1)
+
+ca_test.q255 = pd.Series(ca_test.q255, dtype="int")
+ca_test.q255 = pd.Series(ca_test.q255, dtype="string")
+ca_test.resp_q255 = ca_test.apply(lambda row: row["q255"] == "89547", axis=1)
+
+
+# %% Sum correct answers
+cognitive_score = ca_test[list(ca_test.filter(regex="^resp"))].sum(axis=1)
+ca_test["cognitive_score"] = cognitive_score
+
+
+# %% Print the new lenght of data
+print(f"The new lenght of data is {len(ca_test)} rows.")
+
+
+# %% Show summary of data
+print(ca_test.cognitive_score.describe())
+
+
+# %%
+sns.histplot(ca_test.cognitive_score, kde=True, bins=6)
+
+
+# %%
+palette = sns.color_palette("husl")
+sns.palplot(palette)
+
+
+# %% q997 = Are you a cat person or a dog person? (boxplot)
+sns.catplot(x=ca_test.q997, y=ca_test.cognitive_score,
+ kind="box", height=5, aspect=2,
+ data=ca_test).set_axis_labels(
+ "q997 = Are you a cat person or a dog person?", "cognitive score")
+sns.set_palette(palette)
+
+
+# %% q997 = Are you a cat person or a dog person? (barplot)
+fig_dims = (12, 8)
+fig, ax = plt.subplots(figsize=fig_dims)
+sns.barplot(x=ca_test.q997, y=ca_test.cognitive_score, ax=ax, data=ca_test)
+sns.set_palette(palette)
+plt.xlabel("q997 = Are you a cat person or a dog person?")
+plt.ylabel("cognitive score")
+
+
+# %% See the figures:
+dog_or_cat = ca_test.groupby("q997")["cognitive_score"].describe()
+
+
+# %% ANOVA for dog or cat preferences
+dog_or_cat_pivot = ca_test.pivot(columns="q997", values="cognitive_score")
+dog_or_cat_pivot.drop(dog_or_cat_pivot.columns[0], axis=1, inplace=True)
+
+dog_or_cat_col_names = list(dog_or_cat_pivot.columns)
+dog_or_cat_pivot.columns = ["A", "B", "C", "D"]
+dog_or_cat_pivot.dropna(how="all", inplace=True)
+
+dog_or_cat_samples = [dog_or_cat_pivot[col].dropna() for col in dog_or_cat_pivot]
+f_value, p_value = stats.f_oneway(*dog_or_cat_samples)
+
+
+num_groups = len(dog_or_cat_pivot.columns)
+num_observations = len(dog_or_cat_pivot)
+dfn = num_groups - 1
+dfd = num_observations - num_groups
+
+f_critical = stats.f.ppf(q=0.95, dfn=dfn, dfd=dfd)