將值從另一列python pandas轉移到另一列

Question

我是Python的新手，面對並發布以下內容：

a）如果在Total總計包含NAN總數，如何將值從Rank轉換為Total，使用Rank到Bronze的值

b）如何將Rank的缺失值（在移位值之后）填充到從其上方的行導出的值。

問題：

    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

預期結果：

    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]

1

Answer 1

我會這樣做，加上金，銀和銅（有一些重量，以確保黃金計數更多任何數量的銀等）然后你可以使用rank ：

In [11]: (df["Gold"] * 10000 + df["Silver"] * 100 + df["Bronze"])
Out[11]:
0    463738
1     30100
2     20607
3     20603
4     20602
5     20603
6     10000
7       401
dtype: int64

In [12]: (df["Gold"] * 10000 + df["Silver"] * 100 + df["Bronze"]).rank(method='first', ascending=False)
Out[12]:
0    1.0
1    2.0
2    3.0
3    4.0
4    6.0
5    5.0
6    7.0
7    8.0
dtype: float64

Answer 2

這就是我做到的。 它有效，但不確定它是如何優化的。 只要答案對我來說是正確的。

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'})