total_row = pd.Series怎么用
时间: 2024-05-06 11:16:00 浏览: 10
`total_row` 可以用于 Pandas DataFrame 中的 `append()` 方法来添加一行总计或汇总信息。下面是一个示例:
``` python
import pandas as pd
# 创建一个 DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# 计算总计信息
total_row = pd.Series(df.sum(), name='Total')
# 将总计信息添加到 DataFrame 中
df = df.append(total_row)
print(df)
```
输出结果:
```
A B C
0 1 4 7
1 2 5 8
2 3 6 9
Total 6 15 24
```
在这个例子中,我们首先创建了一个 DataFrame,然后使用 `sum()` 方法计算了每列的总和,并将其存储在一个 Pandas Series 对象 `total_row` 中。最后,我们使用 `append()` 方法将 `total_row` 添加到 DataFrame 中。
相关问题
WOE信用评分卡 python
WOE信用评分卡是一种基于WOE(Weight of Evidence)转换的信用评分卡模型,常用于风控领域。在Python中,我们可以使用pandas库和sklearn库中的一些模块来实现WOE评分卡模型的构建。
首先,我们需要对数据进行分箱处理,并计算每个分箱中好坏样本的数量,从而计算出每个分箱中好坏样本的比例和WOE值。然后,我们可以使用LogisticRegression模型进行拟合,得到每个特征的系数,进而计算出每个样本的分数。
下面是一个简单的示例代码:
```python
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
# 分箱函数
def binning(col, target, max_bins=10):
bins = pd.qcut(col, max_bins, duplicates='drop')
grouped = df.groupby(bins)[target].agg(['count', 'sum'])
grouped['bad_rate'] = grouped['sum'] / grouped['count']
return grouped
# 计算WOE值
def calc_woe(grouped):
total_good = grouped['sum'].sum()
total_bad = grouped['count'].sum() - total_good
woe = pd.Series()
for idx, row in grouped.iterrows():
good = row['sum']
bad = row['count'] - good
woe[idx] = np.log((good / total_good) / (bad / total_bad))
return woe
# 数据导入
df = pd.read_csv('credit.csv')
# 分箱处理
binning_result = binning(df['age'], df['target'])
woe_age = calc_woe(binning_result)
# LogisticRegression模型拟合
X = pd.cut(df['age'], bins=binning_result.index, labels=woe_age)
y = df['target']
lr = LogisticRegression()
lr.fit(X.to_frame(), y)
# 计算AUC值
y_prob = lr.predict_proba(X.to_frame())[:, 1]
auc = roc_auc_score(y, y_prob)
print('AUC score:', auc)
```
后续统计都只针对tournament=='FIFA World Cup' 的世界杯正赛。筛选出世界杯正赛'FIFA World Cup', 保存为w数据框对象。然后统计参加过世界杯正赛的一共有多少个国家? 国家数目保存为 countrynum变量。然后计算计算世界杯比赛中胜率最高的前5个队?将队伍名称及胜率保存在 top5 列表中。 约定: 胜率 = 获胜次数/总场次, 总场次为 (胜 + 平 + 负),使用循环加字典和pandas两种方式完成。然后统计获胜场次最多的是哪个国家? 存在 wincountry 变量。然后统计各个国家在世界杯比赛上各自打入多少个球?将统计结果在 scountry 这个Series对象中。 注意进球分别保存在 home_team(主队进球)和 away_score(客队进球)两列中,进球数应合并计算这两列。然后统计巴西队在世界杯上相对来说,对哪个国家的胜率最低? 将此国家名存在 country变量
根据题目的要求,我们需要针对世界杯正赛('FIFA World Cup')进行统计。可以使用以下代码来筛选出世界杯正赛的比赛并保存为w数据框对象:
```python
# 筛选出世界杯正赛的比赛
w = df[df['tournament'] == 'FIFA World Cup'].reset_index(drop=True)
```
其中`reset_index(drop=True)`是为了重新设置索引。
统计参加过世界杯正赛的国家数目可以使用以下代码:
```python
# 统计参加过世界杯正赛的国家数目
countrynum = len(w['home_team'].unique())
print('参加过世界杯正赛的国家数目:', countrynum)
```
其中`unique()`函数用来获取唯一的值。
计算胜率最高的前5个队可以使用以下两种方式:
(1) 循环加字典
```python
# 循环加字典的方式计算胜率
win_dict = {}
total_dict = {}
for index, row in w.iterrows():
# 计算主队的胜平负情况
if row['home_score'] > row['away_score']:
if row['home_team'] not in win_dict:
win_dict[row['home_team']] = 1
else:
win_dict[row['home_team']] += 1
if row['home_team'] not in total_dict:
total_dict[row['home_team']] = 1
else:
total_dict[row['home_team']] += 1
elif row['home_score'] == row['away_score']:
if row['home_team'] not in total_dict:
total_dict[row['home_team']] = 1
else:
total_dict[row['home_team']] += 1
else:
if row['home_team'] not in total_dict:
total_dict[row['home_team']] = 1
else:
total_dict[row['home_team']] += 1
# 计算客队的胜平负情况
if row['home_score'] < row['away_score']:
if row['away_team'] not in win_dict:
win_dict[row['away_team']] = 1
else:
win_dict[row['away_team']] += 1
if row['away_team'] not in total_dict:
total_dict[row['away_team']] = 1
else:
total_dict[row['away_team']] += 1
elif row['home_score'] == row['away_score']:
if row['away_team'] not in total_dict:
total_dict[row['away_team']] = 1
else:
total_dict[row['away_team']] += 1
else:
if row['away_team'] not in total_dict:
total_dict[row['away_team']] = 1
else:
total_dict[row['away_team']] += 1
# 计算胜率
win_rate_dict = {}
for key, value in total_dict.items():
if key not in win_dict:
win_dict[key] = 0
win_rate_dict[key] = win_dict[key] / value
# 获取胜率最高的前5个队
top5 = sorted(win_rate_dict.items(), key=lambda x: x[1], reverse=True)[:5]
print('胜率最高的前5个队:', top5)
```
(2) pandas方式
```python
# pandas方式计算胜率
matches = w['home_team'].append(w['away_team']).reset_index(drop=True)
wins = pd.Series(matches.groupby(matches).apply(lambda x: ((x == x) & (x > 0)).sum()))
totals = pd.Series(matches.groupby(matches).size())
win_rate = wins / totals
# 获取胜率最高的前5个队
top5 = win_rate.sort_values(ascending=False)[:5]
print('胜率最高的前5个队:', list(zip(top5.index, top5.values)))
```
其中`lambda`函数用来计算胜率,`sort_values`函数用来排序。
统计获胜场次最多的国家可以使用以下代码:
```python
# 统计获胜场次最多的国家
wincountry = w.groupby('home_team')['home_score'].sum().idxmax()
print('获胜场次最多的国家:', wincountry)
```
其中`idxmax()`函数用来获取最大值的索引。
统计各个国家在世界杯比赛上各自打入多少个球可以使用以下代码:
```python
# 统计各个国家在世界杯比赛上各自打入多少个球
scountry = pd.Series(index=w['home_team'].unique(), data=0)
scountry = scountry.add(w.groupby('home_team')['home_score'].sum(), fill_value=0)
scountry = scountry.add(w.groupby('away_team')['away_score'].sum(), fill_value=0)
print('各个国家在世界杯比赛上各自打入的球数:\n', scountry)
```
其中`add`函数用来将两个Series进行合并,`fill_value`参数用来填充缺失值。
统计巴西队在世界杯上相对来说,对哪个国家的胜率最低可以使用以下代码:
```python
# 统计巴西队在世界杯上相对来说,对哪个国家的胜率最低
brazil = w[(w['home_team'] == 'Brazil') | (w['away_team'] == 'Brazil')]
brazil_win = brazil[brazil['home_team'] == 'Brazil']['home_score'] > brazil[brazil['home_team'] == 'Brazil']['away_score']
brazil_win = brazil_win.add(brazil[brazil['away_team'] == 'Brazil']['away_score'] > brazil[brazil['away_team'] == 'Brazil']['home_score'], fill_value=0)
brazil_total = brazil_win.count()
brazil_lose = brazil_total - brazil_win.sum()
lose_rate = {}
for country in brazil['home_team'].append(brazil['away_team']).unique():
if country == 'Brazil':
continue
against = brazil[(brazil['home_team'] == country) | (brazil['away_team'] == country)]
against_win = against[against['home_team'] == country]['home_score'] > against[against['home_team'] == country]['away_score']
against_win = against_win.add(against[against['away_team'] == country]['away_score'] > against[against['away_team'] == country]['home_score'], fill_value=0)
against_total = against_win.count()
against_lose = against_total - against_win.sum()
lose_rate[country] = against_lose / against_total
country = min(lose_rate, key=lose_rate.get)
print('巴西队在世界杯上相对来说,对胜率最低的国家:', country)
```
其中,`brazil`数据框用来筛选出巴西队参加的比赛,然后计算胜率最低的国家。