pandas 将数字转换为熊猫数据框中的 2 位浮点数
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时间：2020-09-14 05:20:14 来源:igfitidea点击:
Converting numbers to 2 digits float number in pandas dataframe
提问by Meruemu
I have a pandas dataframe as follow:
我有一个Pandas数据框如下：
Names Cider Juice Subtotal (Cider) Subtotal (Juice) Total
Richard 13 9 $ 71.5 $ 40.5 $ 112.0
George 7 21 $ 38.5 $ 94.5 $ 133.0
Paul 0 23 $ 0.0 $ 103.5 $ 103.5
John 22 5 $ 121.0 $ 22.5 $ 143.5
Total 42 58 $ 231.0 $ 261.0 $ 492.0
Average 10.5 14.5 $ 57.75 $ 65.25 $ 123.0
I'd like to all float numbers to be '.2f' (2 digits float) numbers. .applymap()does not work since I have string type in "Names" column. Is there a work around using .applymap()or is there a better way to do this?
我希望所有浮点数都是“.2f”（2 位浮点数）数字。.applymap()不起作用，因为我在“名称”列中有字符串类型。是否有解决方法.applymap()或有更好的方法来做到这一点？
import pandas as pd
df = pd.DataFrame(columns=["Names", "Cider", "Juice", "Subtotal(Cider)", "Subtotal(Juice)", "Total"])
people_ordered = input('How many people ordered? ') # type str
# Create the 4x3 table from user input
for i in range(int(people_ordered)):
names = input("Enter the name of Person #{}: ".format(i+1)) # type str
cider_orderred = float(input("How many orders of cider did {} have? ".format(names))) # type str -> int
#cider_orderred = float("{:.2f}".format(cider_orderred))
juice_orderred = float(input("How many orders of juice did {} have? ".format(names))) # type str -> int
#juice_orderred = float("{:.2f}".format(juice_orderred))
# store the values of the subtotals from user inputs
cider_sub = 5.50 * cider_orderred # type float
cider_sub = float("{:.2f}".format(cider_sub))
juice_sub = 4.50 * juice_orderred # type float
juice_sub = float("{:.2f}".format(juice_sub))
total = cider_sub + juice_sub # type float
total = float("{:.2f}".format(total))
# create the 4x6 table
df1 = pd.DataFrame(
data=[[names, int(cider_orderred), int(juice_orderred), round(cider_sub, 2), round(juice_sub, 2), round(total, 2)]],
columns=["Names", "Cider", "Juice", "Subtotal(Cider)", "Subtotal(Juice)", "Total"])
# merge the the 4x3 into the 4x6 table
df = pd.concat([df, df1], axis=0)
# add rows of "Total" and "Average"
df.loc['Total'] = df.sum()
df.loc['Average'] = df[:int(people_ordered)].mean()
# Adding "$" to the prices
df['Subtotal(Cider)'] = '$ ' + df['Subtotal(Cider)'].astype(str)
df['Subtotal(Juice)'] = '$ ' + df['Subtotal(Juice)'].astype(str)
df['Total'] = '$ ' + df['Total'].astype(str)
# Set the row name to "Total" and "Average"
df.iloc[int(people_ordered),0] = 'Total'
df.iloc[int(people_ordered)+1,0] = 'Average'
# Set the index according to 'Names'
df.index = range(len(df.index))
df.set_index('Names', inplace=True)
print(df)
updated with my current solution as above.
更新了我当前的解决方案，如上所述。
回答by jezrael
Use:
用：
set_indexfor only numeric columnsreplace$with one or more whitespaces\s+- convert to
floats byastype - convert to custom format by
applymap
df = (df.set_index('Names')
.replace('$\s+','', regex=True)
.astype(float)
.applymap('{:,.2f}'.format))
print (df)
Cider Juice Subtotal (Cider) Subtotal (Juice) Total
Names
Richard 13.00 9.00 71.50 40.50 112.00
George 7.00 21.00 38.50 94.50 133.00
Paul 0.00 23.00 0.00 103.50 103.50
John 22.00 5.00 121.00 22.50 143.50
Total 42.00 58.00 231.00 261.00 492.00
Average 10.50 14.50 57.75 65.25 123.00
EDIT:
编辑：
I try improve your solution:
我尝试改进您的解决方案：
people_ordered = input('How many people ordered? ')
Data = []
# Create the 4x3 table from user input
for i in range(int(people_ordered)):
names = input("Enter the name of Person #{}: ".format(i+1)) # type str
cider_orderred = int(input("How many orders of cider did {} have? ".format(names))) # type str -> int
juice_orderred = int(input("How many orders of juice did {} have? ".format(names))) # type str -> int
#create in loop tuple and append to list Data
Data.append((names, cider_orderred, juice_orderred))
#create DataFrame form list of tuples, create index by Names
df1 = pd.DataFrame(Data, columns=['Names','Cider','Juice']).set_index('Names')
#count all new columns, rows
df1['Subtotal(Cider)'] = df1['Cider'] * 5.5
df1['Subtotal(Juice)'] = df1['Juice'] * 4.5
df1['Total'] = df1['Subtotal(Cider)'] + df1['Subtotal(Juice)']
df1.loc['Total'] = df1.sum()
#remove row Total for correct mean
df1.loc['Average'] = df1.drop('Total').mean()
#get custom format of columns in list cols
cols = ['Subtotal(Cider)','Subtotal(Juice)','Total']
df1[cols] = df1[cols].applymap('$ {:,.2f}'.format)
#create column from index
df1 = df1.reset_index()
print(df1)
Names Cider Juice Subtotal(Cider) Subtotal(Juice) Total
0 r 13.0 9.0 $ 71.50 $ 40.50 $ 112.00
1 g 7.0 21.0 $ 38.50 $ 94.50 $ 133.00
2 p 0.0 23.0 $ 0.00 $ 103.50 $ 103.50
3 j 22.0 5.0 $ 121.00 $ 22.50 $ 143.50
4 Total 42.0 58.0 $ 231.00 $ 261.00 $ 492.00
5 Average 10.5 14.5 $ 57.75 $ 65.25 $ 123.00
