Python 将值从另一列移到另一列
我是Python新手，面临以下问题：Python 将值从另一列移到另一列,python,pandas,Python,Pandas,我是Python新手，面临以下问题： Rank NOC Gold Silver Bronze Total 0 1 United States (USA) 46 37 38 121 1 2 Argentina (ARG) 3 1 0 4 2 3 Denmark (DEN) 2 6 7 15 3 4 Sweden (SWE) 2 6 3 11 4 5 South Africa (RSA
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
NAN Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
我会将金、银和铜相加（加上一些砝码，以确保金的数量大于任何数量的银等），然后您可以使用：
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
我会将金、银和铜相加（加上一些砝码，以确保金比任何数量的银都多），然后您可以使用：
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
我就是这样做的。它可以工作，但不确定它的优化程度。只要答案对我来说是正确的
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
from pandas import DataFrame, Series
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import re
# Step 1
# Cleanup values within NOC and Rank. Start off with changing the values within Total
# Replace the value of Total which is Null to NaN
df.loc[:, 'Total2'] = df['Total'].isnull()
# Step 2
# Filter Total equal to Nan and shift the row values from Rank to Total - Rank to Bronze
df.ix[df.Total2 == True, 'Total'] = df['Bronze']
df.ix[df.Total2 == True, 'Bronze'] = df['Silver']
df.ix[df.Total2 == True, 'Silver'] = df['Gold']
df.ix[df.Total2 == True, 'Gold'] = df['NOC']
df.ix[df.Total2 == True, 'NOC'] = df['Rank']
# Step 3
# Clean up the Rank column. Create a new column which reveal only digit value
df['Rank2'] = pd.to_numeric(df['Rank'], errors='coerce')
df['fill_forward'] = df['Rank2'].fillna(method='ffill')
del df['Rank']
del df['Rank2']
del df['Total2']
df = df.rename(columns={'fill_forward': 'Rank'})
我就是这样做的。它可以工作，但不确定它的优化程度。只要答案对我来说是正确的
Rank NOC Gold Silver Bronze Total
0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 Tajikistan (TJK) 1 0 0 1 NaN**
7 7 Malaysia (MAS) 0 4 1 5
Rank NOC Gold Silver Bronze Total
[0 1 United States (USA) 46 37 38 121
1 2 Argentina (ARG) 3 1 0 4
2 3 Denmark (DEN) 2 6 7 15
3 4 Sweden (SWE) 2 6 3 11
4 5 South Africa (RSA) 2 6 2 10
5 6 Sweden (SWE) 2 6 3 11
**6 6 Tajikistan (TJK) 1 0 0 1**
7 7 Malaysia (MAS) 0 4 1 5]
from pandas import DataFrame, Series
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import re
# Step 1
# Cleanup values within NOC and Rank. Start off with changing the values within Total
# Replace the value of Total which is Null to NaN
df.loc[:, 'Total2'] = df['Total'].isnull()
# Step 2
# Filter Total equal to Nan and shift the row values from Rank to Total - Rank to Bronze
df.ix[df.Total2 == True, 'Total'] = df['Bronze']
df.ix[df.Total2 == True, 'Bronze'] = df['Silver']
df.ix[df.Total2 == True, 'Silver'] = df['Gold']
df.ix[df.Total2 == True, 'Gold'] = df['NOC']
df.ix[df.Total2 == True, 'NOC'] = df['Rank']
# Step 3
# Clean up the Rank column. Create a new column which reveal only digit value
df['Rank2'] = pd.to_numeric(df['Rank'], errors='coerce')
df['fill_forward'] = df['Rank2'].fillna(method='ffill')
del df['Rank']
del df['Rank2']
del df['Total2']
df = df.rename(columns={'fill_forward': 'Rank'})
如果该数据为表格格式，则更容易阅读并增加有用答案的概率。如果该数据为表格格式，则更容易阅读并增加有用答案的概率。同意。但这并不能解决第二个问题，即“塔吉克斯坦”也应该与“瑞典”排名相同@KaSulaiman这是
method='first'.rank（升序=False）method='first'.rank（升序=False）