Merge pull request #669 from gisce/improve/61128_filtrar_per_models_i…

…gual_I

B9: IMP Filtrar datos por modelo igual a 'I':Inventario)

libcnmc/cir_8_2021/FB9.py

@@ -86,6 +86,7 @@ def consumer(self):
                 columns = [str(x) for x in range(35)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns,
                                  dtype={'0': 'object', '21': 'object', '23': 'object', '34': 'object'})
+                df = df[df['16'] == 'I']
                 df['28'] = pd.to_numeric(df['28'], errors='coerce').astype(float)
                 df['22'] = pd.to_numeric(df['22'], errors='coerce').astype(float)
                 at = OrderedDict()
@@ -165,6 +166,7 @@ def consumer(self):
                 columns = [str(x) for x in range(35)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns,
                                  dtype={'0': 'object', '21': 'object', '23': 'object', '34': 'object'})
+                df = df[df['16'] == 'I']
                 df['28'] = pd.to_numeric(df['28'], errors='coerce').astype(float)
                 df['22'] = pd.to_numeric(df['22'], errors='coerce').astype(float)
                 bt = OrderedDict()
@@ -243,6 +245,7 @@ def consumer(self):
                 file_path = '/tmp/8_2021_loaded_or_generated_b4.txt'
                 columns = [str(x) for x in range(27)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns)
+                df = df[df['8'] == 'I']
                 df['24'] = pd.to_numeric(df['24'], errors='coerce').astype(float)
                 df['14'] = pd.to_numeric(df['14'], errors='coerce').astype(float)
                 pos = OrderedDict()
@@ -321,6 +324,7 @@ def consumer(self):
                 file_path = '/tmp/8_2021_loaded_or_generated_b5.txt'
                 columns = [str(x) for x in range(25)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns)
+                df = df[df['7'] == 'I']
                 df['21'] = pd.to_numeric(df['21'], errors='coerce').astype(float)
                 df['12'] = pd.to_numeric(df['12'], errors='coerce').astype(float)
                 trafo = OrderedDict()
@@ -395,6 +399,7 @@ def consumer(self):
                 file_path = '/tmp/8_2021_loaded_or_generated_b6.txt'
                 columns = [str(x) for x in range(31)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns)
+                df = df[df['17'] == 'I']
                 df['27'] = pd.to_numeric(df['27'], errors='coerce').astype(float)
                 df['19'] = pd.to_numeric(df['19'], errors='coerce').astype(float)
                 cel = OrderedDict()
@@ -470,6 +475,7 @@ def consumer(self):
                 file_path = '/tmp/8_2021_loaded_or_generated_b2.txt'
                 columns = [str(x) for x in range(36)]
                 df = pd.read_csv(file_path, sep=';', decimal=',', names=columns)
+                df = df[df['17'] == 'I']
                 df['31'] = pd.to_numeric(df['31'], errors='coerce').astype(float)
                 df['23'] = pd.to_numeric(df['23'], errors='coerce').astype(float)
                 df['8'] = pd.to_numeric(df['8'], errors='coerce').astype(float)