优化 Excel 到 Pandas 从宽数据到长数据的导入和转换
Optimizing an Excel to Pandas import and transformation from wide to long data
我需要导入和转换 xlsx 文件。它们以宽格式编写，我需要从每一行中复制一些单元格信息并将其与所有其他行中的信息配对：
[编辑：更改格式以表示更复杂的要求]
源格式
ID
Property
Activity1name
Activity1timestamp
Activity2name
Activity2timestamp
1
A
a
1.1.22 00:00
b
2.1.22 10:05
2
B
a
1.1.22 03:00
b
5.1.22 20:16
目标格式
ID
Property
Activity
Timestamp
1
A
a
1.1.22 00:00
1
A
b
2.1.22 10:05
2
B
a
1.1.22 03:00
2
B
b
5.1.22 20:16
下面的代码可以很好地转换数据，但是这个过程真的非常慢：
def transform(data_in):
data = pd.DataFrame(columns=columns)
# Determine number of processes entered in a single row of the original file
steps_per_row = int((data_in.shape[1] - (len(columns) - 2)) / len(process_matching) + 1)
data_in = data_in.to_dict("records") # Convert to dict for speed optimization
for row_dict in tqdm(data_in): # Iterate over each row of the original file
new_row = {}
# Set common columns for each process step
for column in column_matching:
new_row[column] = row_dict[column_matching[column]]
for step in range(0, steps_per_row):
rep = str(step+1) if step > 0 else ""
# Iterate for as many times as there are process steps in one row of the original file and
# set specific columns for each process step, keeping common column values identical for current row
for column in process_matching:
new_row[column] = row_dict[process_matching[column]+rep]
data = data.append(new_row, ignore_index=True) # append dict of new_row to existing data
data.index.name = "SortKey"
data[timestamp].replace(r'.000', '', regex=True, inplace=True) # Remove trailing zeros from timestamp # TODO check if works as intended
data.replace(r'^\s*$', float('NaN'), regex=True, inplace=True) # Replace cells with only spaces with nan
data.dropna(axis=0, how="all", inplace=True) # Remove empty rows
data.dropna(axis=1, how="all", inplace=True) # Remove empty columns
data.dropna(axis=0, subset=[timestamp], inplace=True) # Drop rows with empty Timestamp
data.fillna('', inplace=True) # Replace NaN values with empty cells
return data
显然，遍历每一行甚至每一列根本不是如何使用 pandas 的正确方法，但我看不出如何对这种转换进行矢量化。
我尝试过使用并行化 (modin) 并尝试过是否使用 dict，但它没有用/帮助。脚本的其余部分实际上只是打开和保存文件，所以问题就出在这里。
如果有任何关于如何提高速度的想法，我将不胜感激！
df.melt 函数应该能够更快地执行此类操作。
df = pd.DataFrame({'ID' : [1, 2],
'Property' : ['A', 'B'],
'Info1' : ['x', 'a'],
'Info2' : ['y', 'b'],
'Info3' : ['z', 'c'],
})
data=df.melt(id_vars=['ID','Property'], value_vars=['Info1', 'Info2', 'Info3'])
** 编辑以解决修改后的问题 **
结合 df.melt 和 df.pivot 操作。
# create data
df = pd.DataFrame({'ID' : [1, 2, 3],
'Property' : ['A', 'B', 'C'],
'Activity1name' : ['a', 'a', 'a'],
'Activity1timestamp' : ['1_1_22', '1_1_23', '1_1_24'],
'Activity2name' : ['b', 'b', 'b'],
'Activity2timestamp' : ['2_1_22', '2_1_23', '2_1_24'],
})
# melt dataframe
df_melted = df.melt(id_vars=['ID','Property'],
value_vars=['Activity1name', 'Activity1timestamp',
'Activity2name', 'Activity2timestamp',],
)
# merge categories, i.e. Activity1name Activity2name become Activity
df_melted.at[df_melted['variable'].str.contains('name'), 'variable'] = 'Activity'
df_melted.at[df_melted['variable'].str.contains('timestamp'),'variable'] = 'Timestamp'
# add category ids (dataframe may need to be sorted before this operation)
u_category_ids = np.arange(1,len(df_melted.variable.unique())+1)
category_ids = np.repeat(u_category_ids,len(df)*2).astype(str)
df_melted.insert(0, 'unique_id', df_melted['ID'].astype(str) +'_'+ category_ids)
# pivot table
table = df_melted.pivot_table(index=['unique_id','ID','Property',],
columns='variable', values='value',
aggfunc=lambda x: ' '.join(x))
table = table.reset_index().drop(['unique_id'], axis=1)
按照@Pantelis 的建议，使用pd.melt，我能够极大地加快这种转变，这令人难以置信。以前，一个约 13k 行的文件在 brand-new ThinkPad X1 上需要 4-5 个小时 - 现在只需不到 2 分钟！速度提高了 150 倍，哇。 :)
这是我的新代码，如果有人有类似的数据结构，可以作为灵感/参考：
def transform(data_in):
# Determine number of processes entered in a single row of the original file
steps_per_row = int((data_in.shape[1] - len(column_matching)) / len(process_matching) )
# Specify columns for pd.melt, transforming wide data format to long format
id_columns = column_matching.values()
var_names = {"Erledigungstermin Auftragsschrittbeschreibung":data_in["Auftragsschrittbeschreibung"].replace(" ", np.nan).dropna().values[0]}
var_columns = ["Erledigungstermin Auftragsschrittbeschreibung"]
for _ in range(2, steps_per_row+1):
try:
var_names["Erledigungstermin Auftragsschrittbeschreibung" + str(_)] = data_in["Auftragsschrittbeschreibung" + str(_)].replace(" ", np.nan).dropna().values[0]
except IndexError:
var_names["Erledigungstermin Auftragsschrittbeschreibung" + str(_)] = data_in.loc[0,"Auftragsschrittbeschreibung" + str(_)]
var_columns.append("Erledigungstermin Auftragsschrittbeschreibung" + str(_))
data = pd.melt(data_in, id_vars=id_columns, value_vars=var_columns, var_name="ActivityName", value_name=timestamp)
data.replace(var_names, inplace=True) # Replace "Erledigungstermin Auftragsschrittbeschreibung" with ActivityName
data.sort_values(["Auftrags-\npositionsnummer",timestamp], ascending=True, inplace=True)
# Improve column names
data.index.name = "SortKey"
column_names = {v: k for k, v in column_matching.items()}
data.rename(mapper=column_names, axis="columns", inplace=True)
data[timestamp].replace(r'.000', '', regex=True, inplace=True) # Remove trailing zeros from timestamp
data.replace(r'^\s*$', float('NaN'), regex=True, inplace=True) # Replace cells with only spaces with nan
data.dropna(axis=0, how="all", inplace=True) # Remove empty rows
data.dropna(axis=1, how="all", inplace=True) # Remove empty columns
data.dropna(axis=0, subset=[timestamp], inplace=True) # Drop rows with empty Timestamp
data.fillna('', inplace=True) # Replace NaN values with empty cells
return data
我需要导入和转换 xlsx 文件。它们以宽格式编写，我需要从每一行中复制一些单元格信息并将其与所有其他行中的信息配对：
[编辑：更改格式以表示更复杂的要求]
源格式
| ID | Property | Activity1name | Activity1timestamp | Activity2name | Activity2timestamp |
|---|---|---|---|---|---|
| 1 | A | a | 1.1.22 00:00 | b | 2.1.22 10:05 |
| 2 | B | a | 1.1.22 03:00 | b | 5.1.22 20:16 |
目标格式
| ID | Property | Activity | Timestamp |
|---|---|---|---|
| 1 | A | a | 1.1.22 00:00 |
| 1 | A | b | 2.1.22 10:05 |
| 2 | B | a | 1.1.22 03:00 |
| 2 | B | b | 5.1.22 20:16 |
下面的代码可以很好地转换数据，但是这个过程真的非常慢：
def transform(data_in):
data = pd.DataFrame(columns=columns)
# Determine number of processes entered in a single row of the original file
steps_per_row = int((data_in.shape[1] - (len(columns) - 2)) / len(process_matching) + 1)
data_in = data_in.to_dict("records") # Convert to dict for speed optimization
for row_dict in tqdm(data_in): # Iterate over each row of the original file
new_row = {}
# Set common columns for each process step
for column in column_matching:
new_row[column] = row_dict[column_matching[column]]
for step in range(0, steps_per_row):
rep = str(step+1) if step > 0 else ""
# Iterate for as many times as there are process steps in one row of the original file and
# set specific columns for each process step, keeping common column values identical for current row
for column in process_matching:
new_row[column] = row_dict[process_matching[column]+rep]
data = data.append(new_row, ignore_index=True) # append dict of new_row to existing data
data.index.name = "SortKey"
data[timestamp].replace(r'.000', '', regex=True, inplace=True) # Remove trailing zeros from timestamp # TODO check if works as intended
data.replace(r'^\s*$', float('NaN'), regex=True, inplace=True) # Replace cells with only spaces with nan
data.dropna(axis=0, how="all", inplace=True) # Remove empty rows
data.dropna(axis=1, how="all", inplace=True) # Remove empty columns
data.dropna(axis=0, subset=[timestamp], inplace=True) # Drop rows with empty Timestamp
data.fillna('', inplace=True) # Replace NaN values with empty cells
return data
显然，遍历每一行甚至每一列根本不是如何使用 pandas 的正确方法，但我看不出如何对这种转换进行矢量化。
我尝试过使用并行化 (modin) 并尝试过是否使用 dict，但它没有用/帮助。脚本的其余部分实际上只是打开和保存文件，所以问题就出在这里。
如果有任何关于如何提高速度的想法，我将不胜感激！
df.melt 函数应该能够更快地执行此类操作。
df = pd.DataFrame({'ID' : [1, 2],
'Property' : ['A', 'B'],
'Info1' : ['x', 'a'],
'Info2' : ['y', 'b'],
'Info3' : ['z', 'c'],
})
data=df.melt(id_vars=['ID','Property'], value_vars=['Info1', 'Info2', 'Info3'])
** 编辑以解决修改后的问题 **
结合 df.melt 和 df.pivot 操作。
# create data
df = pd.DataFrame({'ID' : [1, 2, 3],
'Property' : ['A', 'B', 'C'],
'Activity1name' : ['a', 'a', 'a'],
'Activity1timestamp' : ['1_1_22', '1_1_23', '1_1_24'],
'Activity2name' : ['b', 'b', 'b'],
'Activity2timestamp' : ['2_1_22', '2_1_23', '2_1_24'],
})
# melt dataframe
df_melted = df.melt(id_vars=['ID','Property'],
value_vars=['Activity1name', 'Activity1timestamp',
'Activity2name', 'Activity2timestamp',],
)
# merge categories, i.e. Activity1name Activity2name become Activity
df_melted.at[df_melted['variable'].str.contains('name'), 'variable'] = 'Activity'
df_melted.at[df_melted['variable'].str.contains('timestamp'),'variable'] = 'Timestamp'
# add category ids (dataframe may need to be sorted before this operation)
u_category_ids = np.arange(1,len(df_melted.variable.unique())+1)
category_ids = np.repeat(u_category_ids,len(df)*2).astype(str)
df_melted.insert(0, 'unique_id', df_melted['ID'].astype(str) +'_'+ category_ids)
# pivot table
table = df_melted.pivot_table(index=['unique_id','ID','Property',],
columns='variable', values='value',
aggfunc=lambda x: ' '.join(x))
table = table.reset_index().drop(['unique_id'], axis=1)
按照@Pantelis 的建议，使用pd.melt，我能够极大地加快这种转变，这令人难以置信。以前，一个约 13k 行的文件在 brand-new ThinkPad X1 上需要 4-5 个小时 - 现在只需不到 2 分钟！速度提高了 150 倍，哇。 :)
这是我的新代码，如果有人有类似的数据结构，可以作为灵感/参考：
def transform(data_in):
# Determine number of processes entered in a single row of the original file
steps_per_row = int((data_in.shape[1] - len(column_matching)) / len(process_matching) )
# Specify columns for pd.melt, transforming wide data format to long format
id_columns = column_matching.values()
var_names = {"Erledigungstermin Auftragsschrittbeschreibung":data_in["Auftragsschrittbeschreibung"].replace(" ", np.nan).dropna().values[0]}
var_columns = ["Erledigungstermin Auftragsschrittbeschreibung"]
for _ in range(2, steps_per_row+1):
try:
var_names["Erledigungstermin Auftragsschrittbeschreibung" + str(_)] = data_in["Auftragsschrittbeschreibung" + str(_)].replace(" ", np.nan).dropna().values[0]
except IndexError:
var_names["Erledigungstermin Auftragsschrittbeschreibung" + str(_)] = data_in.loc[0,"Auftragsschrittbeschreibung" + str(_)]
var_columns.append("Erledigungstermin Auftragsschrittbeschreibung" + str(_))
data = pd.melt(data_in, id_vars=id_columns, value_vars=var_columns, var_name="ActivityName", value_name=timestamp)
data.replace(var_names, inplace=True) # Replace "Erledigungstermin Auftragsschrittbeschreibung" with ActivityName
data.sort_values(["Auftrags-\npositionsnummer",timestamp], ascending=True, inplace=True)
# Improve column names
data.index.name = "SortKey"
column_names = {v: k for k, v in column_matching.items()}
data.rename(mapper=column_names, axis="columns", inplace=True)
data[timestamp].replace(r'.000', '', regex=True, inplace=True) # Remove trailing zeros from timestamp
data.replace(r'^\s*$', float('NaN'), regex=True, inplace=True) # Replace cells with only spaces with nan
data.dropna(axis=0, how="all", inplace=True) # Remove empty rows
data.dropna(axis=1, how="all", inplace=True) # Remove empty columns
data.dropna(axis=0, subset=[timestamp], inplace=True) # Drop rows with empty Timestamp
data.fillna('', inplace=True) # Replace NaN values with empty cells
return data