def _operation(self, group_df):
nested_group_list = []
for row in group_df:
nested_group_list.append(row)
index_criteria = []
for var in self.vars:
is_desc = False
if var.startswith("desc_"):
is_desc = True
var = var[5:]
index_criterion = group_df.vars.index(var) + 1
if is_desc:
index_criterion *= -1
index_criteria.append(index_criterion)
default_recursion_limit = getrecursionlimit()
setrecursionlimit(len(group_df) + 10)
sorted_group_list = Sort.__merge_sort(nested_group_list,
index_criteria)
setrecursionlimit(default_recursion_limit)
result = DataFrame()
for var in group_df.vars:
result.add_column(var)
for row in sorted_group_list:
result.add_row(row)
return result
def _operation(self, df):
vars_with_criterion = list(self.__criteria.keys())
if any(var not in df.vars for var in vars_with_criterion):
raise KeyError
result = DataFrame()
for var in df.vars:
result.add_column(var)
for i in range(len(df)):
row_dict = df.row_as_dict(i)
add_row = True
for var in vars_with_criterion:
test_result = False
try:
test_result = eval(
str(var) + " " + str(self.__criteria[var]),
{"__builtins__": {}}, row_dict)
except TypeError:
pass
except Exception as e:
raise e
finally:
if isinstance(test_result, bool):
add_row *= test_result
else:
raise TypeError
if add_row:
result.add_row(df[None, i])
return result
def import_json(path, root=None):
"""
Imports a JSON file as DataFrame.
Parameters
----------
path : str
Absolute or relative path to the JSON file to import
root : str = None
Name of the root's node to import ; if None, imports the first root node of the file
Returns
-------
DataFrame
A DataFrame with the contents of the JSON file
"""
with open(path) as jsonfile:
data = json.load(jsonfile)
roots = list(data.keys())
if len(roots) == 1 or root is None:
root = roots[0]
elif root not in roots:
raise KeyError
table = data[root]
table_vars = list(table[0].keys())
df = DataFrame()
for var in table_vars:
df.add_column(var)
for row in table:
df.add_row(list(row.values()))
return df
def apply(self, df):
list_vars = [*df.groups_vars, *self.vars]
df = Select(*list_vars).apply(df)
result = DataFrame()
for var in list_vars:
result.add_column(var)
groups = df.groups_df
for group_df in groups:
row = []
for group_var in df.groups_vars:
row.append(group_df[group_var, 0])
for var in self.vars:
col = group_df[var]
if self.__del_na:
col = [val for val in col if val is not None]
if self.__del_nan:
col = [val for val in col if isinstance(val, Number)]
partial_result = self._operation(col)
if isinstance(partial_result, dict):
keys = list(partial_result.keys())
if (var + "_" + keys[0]) not in result.vars:
last = var
for key in keys:
new_var = var + "_" + key
result.add_column(new_var, after=last)
last = new_var
result.del_column(var)
row.extend(list(partial_result.values()))
else:
row.append(partial_result)
result.add_row(row)
result = GroupBy(*df.groups_vars[:-1]).apply(result)
return result
def apply(self, df):
result = DataFrame()
for var in self.vars:
result.add_column(var, df[var])
kept_group_vars = [var for var in df.groups_vars if var in self.vars]
result = GroupBy(*kept_group_vars).apply(result)
return result
def apply(self, df):
result = DataFrame()
groups = df.groups_df
for group_df in groups:
transformed_group = self._operation(group_df)
if len(transformed_group) > 0:
if len(result.vars) == 0:
for var in transformed_group.vars:
result.add_column(var)
for row in transformed_group:
result.add_row(row)
result = GroupBy(*df.groups_vars).apply(result)
return result
def import_csv(path, headers=True, delimiter=";", encoding='ISO-8859-1'):
"""
Imports a CSV file as DataFrame
Parameters
----------
path : str
Absolute or relative path to the CSV file to import
headers : bool = True
Specify if the file have headers
delimiter : str = ";"
Specify the file's delimiter
encoding : str = 'ISO-8859-1'
Specify the file's encoding
Returns
-------
DataFrame
A DataFrame with the contents of the CSV file
"""
df = DataFrame()
with open(path, newline='', encoding=encoding) as csv_file:
reader = csv.reader(csv_file, delimiter=delimiter)
first_row = True
for row in reader:
if first_row:
if headers:
for var in row:
df.add_column(var)
else:
for i in range(len(row)):
name = "Var" + str(i)
df.add_column(name)
first_row = False
else:
df.add_row(row)
return df
def apply(self, df):
result = DataFrame()
other_vars = [
var for var in self.__other.vars
if var not in list(self.__matches.keys())
]
for var in df.vars:
result.add_column(var)
for var in other_vars:
if var in df.vars:
result.add_column("Y_" + str(var))
else:
result.add_column(var)
known_matches = {}
for i in range(len(df)):
base_row = df[None, i]
filter_kw = {}
filter_str = ""
for key in list(self.__matches.keys()):
target_value = df[self.__matches[key], i]
filter_kw[key] = '=="' + str(target_value) + '"'
filter_str += str(key) + "_" + str(target_value)
if known_matches.get(filter_str) is None:
matches = Filter(**filter_kw).apply(self.__other)
if len(matches) == 0:
other_content = [None] * len(other_vars)
else:
other_content = Select(*other_vars).apply(matches)
known_matches[filter_str] = other_content
else:
other_content = known_matches[filter_str]
if isinstance(other_content, DataFrame):
for row in other_content:
new_row = deepcopy(base_row)
new_row.extend(row)
result.add_row(new_row)
else:
new_row = deepcopy(base_row)
new_row.extend(other_content)
result.add_row(new_row)
return result