<jupyter_start><jupyter_text>Read the Data from the datos.gov.co<jupyter_code>#There is 1 file per semester, since COPACABANA is a small town we want to have at least 2 years of data
#for now in this notebook we just read 2019-2, also the 2nd semester correspond to calendar A Schools
url="https://www.datos.gov.co/api/views/ynam-yc42/rows.csv?accessType=DOWNLOAD"
s=requests.get(url).content
#saber_df_2019_2 = pd.read_csv(io.StringIO(s.decode('utf-8')), engine='python')
saber_df_2019_2 = pd.read_csv(io.StringIO(s.decode('utf-8')))
saber_df_2019_2<jupyter_output><empty_output><jupyter_text>This file has more than 500K rows, we just need the copacabana data<jupyter_code>saber_df_2019_2.columns
saber_copa = saber_df_2019_2[(saber_df_2019_2['ESTU_MCPIO_RESIDE'] == 'COPACABANA')]
saber_otros = saber_df_2019_2[(saber_df_2019_2['ESTU_MCPIO_RESIDE'] != 'COPACABANA')]
saber_copa.shape
print("----------")
print("ESTU_TIPODOCUMENTO")
print(saber_copa.ESTU_TIPODOCUMENTO.unique())
print()
print("num unique values: ", len(saber_copa.ESTU_TIPODOCUMENTO.unique()))
print()
print("----------")
print("ESTU_ETNIA")
print(saber_copa.ESTU_ETNIA.unique())
print()
print("num unique values: ", len(saber_copa.ESTU_ETNIA.unique()))
print()
print("FAMI_TRABAJOLABORMADRE")
print(saber_copa.FAMI_TRABAJOLABORMADRE.unique())
print()
print("num unique values: ", len(saber_copa.FAMI_TRABAJOLABORMADRE.unique()))
print()
print("FAMI_EDUCACIONMADRE")
print(saber_copa.FAMI_EDUCACIONMADRE.unique())
print()
print("num unique values: ", len(saber_copa.FAMI_TRABAJOLABORMADRE.unique()))

plt.axvline(saber_otros['PUNT_GLOBAL'].mean())
sns.distplot(saber_otros['PUNT_GLOBAL'], bins=50, color='r')
print(saber_otros['PUNT_GLOBAL'].mean())
print(saber_copa['PUNT_GLOBAL'].mean())
plt.axvline(saber_copa['PUNT_GLOBAL'].mean(), color='b')
sns.distplot(saber_copa['PUNT_GLOBAL'], bins=50, color='b')
fig, ax = plt.subplots()
plt.axvline(saber_otros['PUNT_GLOBAL'].mean(), color='r')
sns.distplot(saber_otros['PUNT_GLOBAL'], bins=50, color='r')

plt.axvline(saber_copa['PUNT_GLOBAL'].mean(), color='b')
sns.distplot(saber_copa['PUNT_GLOBAL'], bins=50, color='b')
sns.catplot(x = 'PUNT_GLOBAL', y = 'FAMI_EDUCACIONMADRE', kind='box', data=saber_copa, height=7, aspect = 1.3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16900790166812993, 'fraction_numerical': 0.02897278314310799, 'mean_word_length': 8.781115879828326, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '122547', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1026, 'n_tokens_neox': 961, 'n_words': 138, 'path': 'notebooks/1.0-dm-saber-eda.ipynb', 'repo_name': 'ljparrag/ds4ateam67dropout', 'size': '2278'}