def test_dict_normalize_df():
# NOTE: This test is a bit circuitous bc storage of a target df requires
# storing some source from which to generate that df; in this case, we
# are using test_utils_csv_to_df.csv which represents the desired csv
# representation of the df with successful execution of the fxn;
# however, to get our test df in a 1:1 comparable state to the target df
# generated from this csv, we have to first convert the test df to csv
# and then convert it back to a df; an annoying nuance, but necessary for
# true apples-to-apples comparison between dfs generated this way
utils = Utils()
INDEX = "ISIN"
ARGUMENT = "articleList"
with open("./utils/tests/test_utils_dict_to_df.txt") as dict_file:
test_dict = json.load(dict_file)
target_df = pd.read_csv("./utils/tests/test_utils_csv_to_df.csv",
keep_default_na=False)
temp_df = utils.dict_normalize_df(
test_dict,
INDEX,
ARGUMENT,
)
temp_df.to_csv(
"./utils/tests/test_utils_df_to_csv.csv",
sep=",",
index=None,
header=True,
)
test_df = pd.read_csv("./utils/tests/test_utils_df_to_csv.csv",
keep_default_na=False)
assert test_df.equals(target_df)
def returnresult(actionid, result):
options = {
"SN": "00010001",
"CMD": "actionresult",
"actionID": actionid,
"result": result
}
print(options)
print(Utils().http_post("/north/", options))
def test_text_parse_dict():
utils = Utils()
test_filepath = "./utils/tests/test_utils_creds.txt"
test_dict = utils.text_parse_dict(filepath=test_filepath, separator="=")
target_dict = {
"USERNAME": "******",
"PASSWORD": "******",
"APPID": "MyTestApplication",
}
assert test_dict == target_dict
def test_uuid_generator():
utils = Utils()
test_seedvar = 6
test_bitcount = 128
test_refid = "4295905573" # Apple Inc (Organization) RefID
target_uuid_final = "14fe8b5a6ec8d00eddbfb6bd60c2e82e13a59db3"
test_uuid_final = utils.uuid_generator(
seedvar=test_seedvar,
bitcount=test_bitcount,
refstring=test_refid,
)
assert test_uuid_final == target_uuid_final
df.info()
df.shape
Creating a list type variable called **col_remove**, in which the features that are not important for our goal will be added
col_remove = ['id']
## Analysis of Missing Values
df.isna().sum()
Loading a class called Utils, this class helps to vizualize the data
utils = Utils()
utils.plot_variables_nan(df)
utils.df_nan
Removing the **riesgo** variable since it has more than 99% the NaN
df = df.drop(columns=['riesgo'])
df.shape
## Analysis of target value
df[['client']].hist()
plt.ylabel('Count')
def test_text_parse_list():
utils = Utils()
test_filepath = "./utils/tests/test_utils_entities.txt"
test_list = utils.text_parse_list(filepath=test_filepath)
target_list = ["ABCD", "1234", "!@#$"]
assert test_list == target_list
