mirror of
https://github.com/Telecominfraproject/wlan-lanforge-scripts.git
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119 lines
7.0 KiB
Python
119 lines
7.0 KiB
Python
#!/usr/bin/env pythonn3
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import pandas as pd
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class pandas_extensions:
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# ================ Pandas Dataframe Functions ======================================
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# takes any dataframe and returns the specified file extension of it
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def df_to_file(self, output_f=None, dataframe=None, save_path=None):
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if output_f.lower() == 'hdf':
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dataframe.to_hdf(save_path.replace('csv', 'h5', 1), 'table', append=True)
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if output_f.lower() == 'parquet':
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dataframe.to_parquet(save_path.replace('csv', 'parquet', 1), engine='pyarrow')
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if output_f.lower() == 'png':
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fig = dataframe.plot().get_figure()
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fig.savefig(save_path.replace('csv', 'png', 1))
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if output_f.lower() == 'xlsx':
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dataframe.to_excel(save_path.replace('csv', 'xlsx', 1))
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if output_f.lower() == 'json':
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dataframe.to_json(save_path.replace('csv', 'json', 1))
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if output_f.lower() == 'stata':
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dataframe.to_stata(save_path.replace('csv', 'dta', 1))
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if output_f.lower() == 'pickle':
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dataframe.to_pickle(save_path.replace('csv', 'pkl', 1))
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if output_f.lower() == 'html':
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dataframe.to_html(save_path.replace('csv', 'html', 1))
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# takes any format of a file and returns a dataframe of it
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def file_to_df(self, file_name):
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if file_name.split('.')[-1] == 'csv':
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return pd.read_csv(file_name)
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# only works for test_ipv4_variable_time at the moment
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def compare_two_df(self, dataframe_one=None, dataframe_two=None):
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# df one = current report
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# df two = compared report
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pd.set_option("display.max_rows", None, "display.max_columns", None)
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# get all of common columns besides Timestamp, Timestamp milliseconds
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common_cols = list(set(dataframe_one.columns).intersection(set(dataframe_two.columns)))
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cols_to_remove = ['Timestamp milliseconds epoch', 'Timestamp', 'LANforge GUI Build: 5.4.3']
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com_cols = [i for i in common_cols if i not in cols_to_remove]
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# check if dataframes have the same endpoints
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if dataframe_one.name.unique().tolist().sort() == dataframe_two.name.unique().tolist().sort():
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endpoint_names = dataframe_one.name.unique().tolist()
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if com_cols is not None:
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dataframe_one = dataframe_one[[c for c in dataframe_one.columns if c in com_cols]]
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dataframe_two = dataframe_two[[c for c in dataframe_one.columns if c in com_cols]]
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dataframe_one = dataframe_one.loc[:, ~dataframe_one.columns.str.startswith('Script Name:')]
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dataframe_two = dataframe_two.loc[:, ~dataframe_two.columns.str.startswith('Script Name:')]
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lowest_duration = min(dataframe_one['Duration elapsed'].max(), dataframe_two['Duration elapsed'].max())
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print("The max duration in the new dataframe will be... " + str(lowest_duration))
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compared_values_dataframe = pd.DataFrame(
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columns=[col for col in com_cols if not col.startswith('Script Name:')])
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cols = compared_values_dataframe.columns.tolist()
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cols = sorted(cols, key=lambda L: (L.lower(), L))
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compared_values_dataframe = compared_values_dataframe[cols]
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print(compared_values_dataframe)
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for duration_elapsed in range(lowest_duration):
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for endpoint in endpoint_names:
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# check if value has a space in it or is a str.
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# if value as a space, only take value before space for calc, append that calculated value after space.
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# if str. check if values match from 2 df's. if values do not match, write N/A
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for_loop_df1 = dataframe_one.loc[(dataframe_one['name'] == endpoint) & (
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dataframe_one['Duration elapsed'] == duration_elapsed)]
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for_loop_df2 = dataframe_two.loc[(dataframe_one['name'] == endpoint) & (
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dataframe_two['Duration elapsed'] == duration_elapsed)]
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# print(for_loop_df1)
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# print(for_loop_df2)
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cols_to_loop = [i for i in com_cols if
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i not in ['Duration elapsed', 'Name', 'Script Name: test_ipv4_variable_time']]
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cols_to_loop = sorted(cols_to_loop, key=lambda L: (L.lower(), L))
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print(cols_to_loop)
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row_to_append = {}
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row_to_append["Duration elapsed"] = duration_elapsed
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for col in cols_to_loop:
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print(col)
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print(for_loop_df1)
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# print(for_loop_df2)
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print(for_loop_df1.at[0, col])
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print(for_loop_df2.at[0, col])
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if type(for_loop_df1.at[0, col]) == str and type(for_loop_df2.at[0, col]) == str:
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if (' ' in for_loop_df1.at[0, col]):
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# do subtraction
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new_value = float(for_loop_df1.at[0, col].split(" ")[0]) - float(
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for_loop_df2.at[0, col].split(" ")[0])
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# add on last half of string
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new_value = str(new_value) + for_loop_df2.at[0, col].split(" ")[1]
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# print(new_value)
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row_to_append[col] = new_value
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else:
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if for_loop_df1.at[0, col] != for_loop_df2.at[0, col]:
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row_to_append[col] = 'NaN'
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else:
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row_to_append[col] = for_loop_df1.at[0, col]
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elif type(for_loop_df1.at[0, col]) == int and type(for_loop_df2.at[0, col]) == int or type(
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for_loop_df1.at[0, col]) == float and type(for_loop_df2.at[0, col]) == float:
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new_value = for_loop_df1.at[0, col] - for_loop_df2.at[0, col]
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row_to_append[col] = new_value
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compared_values_dataframe = compared_values_dataframe.append(row_to_append, ignore_index=True, )
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print(compared_values_dataframe)
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# add col name to new df
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print(dataframe_one)
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print(dataframe_two)
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print(compared_values_dataframe)
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else:
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ValueError("Unable to execute report comparison due to inadequate file commonalities. ")
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exit(1)
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else:
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ValueError(
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"Two files do not have the same endpoints. Please try file comparison with files that have the same endpoints.")
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exit(1)
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# take those columns and separate those columns from others in DF.
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pass
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# return compared_df
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