我用两个方法NRG_load和NRG_flat创建了一个类。第一个加载CSV，将其转换为DataFrame并应用一些过滤；第二个获取此DataFrame，并在创建两列后melt使DataFrame进行旋转。
我正在使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，并且需要使用'105'作为第二个参数来在方法中运行if循环以创建上述列。
我无法解释的行为是第二行-使用NRG_flat方法的第二行-更改了nrg105的值。
请注意，如果仅运行NRG_load方法，则会得到预期的DataFrame。
我缺少什么行为？因为这不是我第一次应用这样的语法，但是我从来没有遇到过问题，所以我不知道应该看什么。< / p>
预先感谢您的所有建议。
编辑：根据要求，以下是课程的代码：
# -*- 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：如果可以，这是我在IPython中使用EurostatNRG类时收到的错误：
[EurostatNRG的自动重新加载失败：追溯（最近一次通话是最后一次）： 文件 “ C：\ Users \ CAPIZZIF \ AppData \ Local \ Continuum \ anaconda3 \ lib \ site-packages \ IPython \ extensions \ autoreload.py”， 244行，在检查中 superreload（m，重新加载，self.old_objects）文件“ C：\ Users \ CAPIZZIF \ AppData \ Local \ Continuum \ anaconda3 \ lib \ site-packages \ IPython \ extensions \ autoreload.py”， 线394，在超级重载中 update_generic（old_obj，new_obj）文件“ C：\ Users \ CAPIZZIF \ AppData \ Local \ Continuum \ anaconda3 \ lib \ site-packages \ IPython \ extensions \ autoreload.py”， update_generic中的第331行 更新（a，b）文件“ C：\ Users \ CAPIZZIF \ AppData \ Local \ Continuum \ anaconda3 \ lib \ site-packages \ IPython \ extensions \ autoreload.py”， 第279行，在update_class中 如果（old_obj == new_obj）为True：文件“ C：\ Users \ CAPIZZIF \ AppData \ Local \ Continuum \ anaconda3 \ lib \ site-packages \ pandas \ core \ generic.py”， 第1478行，非零 .format（self。 class 。 name ））ValueError：DataFrame的真值不明确。使用a.empty，a.bool（），a.item（），a.any（）或 a.all（）。 ]
答案 0 :(得分：1)
我设法找到了罪魁祸首。
在NRG_flat方法中，行：
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
...
df['PRODUCT'] = df['KEY_PRODUCT'].map(indic_dic)
弄乱了df DataFrame的副本，因此我不得不用Pandas assign method来更改它们：
df = df.assign(INDICATOR=df.KEY_INDICATOR.map(prod_dic))
...
df = df.assign(PRODUCT=df.KEY_PRODUCT.map(prod_dic))
我再也没有错误。
谢谢您的回复！