我用两个方法创建了一个类,NRG_load
和NRG_flat
。第一种方法加载CSV,将其转换为数据帧并应用一些过滤;第二种方法获取这个数据帧,在创建两列之后,它melt
使用数据帧来透视它。你知道吗
我正在用以下代码尝试这些方法:
nrg105 = eNRG.NRG_load('nrg_105a.tsv')
nrg105_flat = eNRG.NRG_flat(nrg105, '105')
其中eNRG
是类,需要使用'105'作为第二个参数在方法中运行if循环来创建前面提到的列。你知道吗
我无法解释的行为是,第二行-使用NRG_flat
方法的行-更改nrg105
值。你知道吗
请注意,如果只运行NRG_load
方法,则会得到预期的数据帧。你知道吗
我错过了什么行为?因为这不是我第一次应用这样的语法,但我从来没有遇到过问题,所以我不知道我应该看哪里。你知道吗
提前谢谢你的建议。你知道吗
编辑:根据要求,以下是课程代码:
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 16 15:22:21 2019
@author: CAPIZZI Filippo Antonio
"""
import pandas as pd
from FixFilename import FixFilename as ff
from SplitColumn import SplitColumn as sc
from datetime import datetime as ddt
class EurostatNRG:
# This class includes the modules needed to load and filter
# the Eurostat NRG files
# Default countries' lists to be used by the functions
COUNTRIES = [
'EU28', 'AL', 'AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'EL',
'ES', 'FI', 'FR', 'GE', 'HR', 'HU', 'IE', 'IS', 'IT', 'LT', 'LU', 'LV',
'MD', 'ME', 'MK', 'MT', 'NL', 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK',
'TR', 'UA', 'UK', 'XK'
]
# Default years of analysis
YEARS = list(range(2005, int(ddt.now().year) - 1))
# NOTE: the 'datetime' library will call the current year, but since
# the code is using the 'range' function, the end years will be always
# current-1 (e.g. if we are in 2019, 'current year' will be 2018).
# Thus, I have added "-1" because the end year is t-2.
INDIC_PROD = pd.read_excel(
'./Datasets/VITO/map_nrg.xlsx',
sheet_name=[
'nrg105a_indic', 'nrg105a_prod', 'nrg110a_indic', 'nrg110a_prod',
'nrg110'
],
convert_float=True)
def NRG_load(dataset, countries=COUNTRIES, years=YEARS, unit='ktoe'):
# This module will load and refine the NRG dataset,
# preparing it to be filtered
# Fix eventual flags
dataset = ff.fix_flags(dataset)
# Load the dataset into a DataFrame
df = pd.read_csv(
dataset,
delimiter='\t',
encoding='utf-8',
na_values=[':', ': ', ' :'],
decimal='.')
# Clean up spaces from the column names
df.columns = df.columns.str.strip()
# Removes the mentioned column because it's not needed
if 'Flag and Footnotes' in df.columns:
df.drop(columns=['Flag and Footnotes'], inplace=True)
# Split the first column into separate columns
df = sc.nrg_split_column(df)
# Rename the columns
df.rename(
columns={
'country': 'COUNTRY',
'fuel_code': 'KEY_PRODUCT',
'nrg_code': 'KEY_INDICATOR',
'unit': 'UNIT'
},
inplace=True)
# Filter the dataset
df = EurostatNRG.NRG_filter(
df, countries=countries, years=years, unit=unit)
return df
def NRG_filter(df, countries, years, unit):
# This module will filter the input DataFrame 'df'
# showing only the 'countries', 'years' and 'unit' selected
# First, all of the units not of interest are removed
df.drop(df[df.UNIT != unit.upper()].index, inplace=True)
# Then, all of the countries not of interest are filtered out
df.drop(df[~df['COUNTRY'].isin(countries)].index, inplace=True)
# Finally, all of the years not of interest are removed,
# and the columns are rearranged according to the desired output
main_cols = ['KEY_INDICATOR', 'KEY_PRODUCT', 'UNIT', 'COUNTRY']
cols = main_cols + [str(y) for y in years if y not in main_cols]
df = df.reindex(columns=cols)
return df
def NRG_flat(df, name):
# This module prepares the DataFrame to be flattened,
# then it gives it as output
# Assign the indicators and products' names
if '105' in name: # 'name' is the name of the dataset
# Creating the 'INDICATOR' column
indic_dic = dict(
zip(EurostatNRG.INDIC_PROD['nrg105a_indic'].KEY_INDICATOR,
EurostatNRG.INDIC_PROD['nrg105a_indic'].INDICATOR))
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
# Creating the 'PRODUCT' column
prod_dic = dict(
zip(
EurostatNRG.INDIC_PROD['nrg105a_prod'].KEY_PRODUCT.astype(
str), EurostatNRG.INDIC_PROD['nrg105a_prod'].PRODUCT))
df['PRODUCT'] = df['KEY_PRODUCT'].map(prod_dic)
elif '110' in name:
# Creating the 'INDICATOR' column
indic_dic = dict(
zip(EurostatNRG.INDIC_PROD['nrg110a_indic'].KEY_INDICATOR,
EurostatNRG.INDIC_PROD['nrg110a_indic'].INDICATOR))
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
# Creating the 'PRODUCT' column
prod_dic = dict(
zip(
EurostatNRG.INDIC_PROD['nrg110a_prod'].KEY_PRODUCT.astype(
str), EurostatNRG.INDIC_PROD['nrg110a_prod'].PRODUCT))
df['PRODUCT'] = df['KEY_PRODUCT'].map(prod_dic)
# Delete che columns 'KEY_INDICATOR' and 'KEY_PRODUCT', and
# rearrange the columns in the desired order
df.drop(columns=['KEY_INDICATOR', 'KEY_PRODUCT'], inplace=True)
main_cols = ['INDICATOR', 'PRODUCT', 'UNIT', 'COUNTRY']
year_cols = [y for y in df.columns if y not in main_cols]
cols = main_cols + year_cols
df = df.reindex(columns=cols)
# Pivot the DataFrame to have it in flat format
df = df.melt(
id_vars=df.columns[:4], var_name='YEAR', value_name='VALUE')
# Convert the 'VALUE' column into float numbers
df['VALUE'] = pd.to_numeric(df['VALUE'], downcast='float')
# Drop rows that have no indicators (it means they are not in
# the Excel file with the products of interest)
df.dropna(subset=['INDICATOR', 'PRODUCT'], inplace=True)
return df
编辑2:如果这有帮助的话,这就是我在IPython中使用EurostatNRG
类时收到的错误:
[autoreload of EurostatNRG failed: Traceback (most recent call last): File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py", line 244, in check superreload(m, reload, self.old_objects) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py", line 394, in superreload update_generic(old_obj, new_obj) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py", line 331, in update_generic update(a, b) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py", line 279, in update_class if (old_obj == new_obj) is True: File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py", line 1478, in nonzero .format(self.class.name)) ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). ]
我设法找到了罪犯。你知道吗
在
NRG_flat
方法中,行:弄乱} method 来更改它们:
df
数据帧的副本,因此我必须用Pandas ^{我不会再犯错误了。你知道吗
感谢您的回复!你知道吗
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