Python 以表格的形式输出结果

我的数据集如下：

print(df)

name      points    attempts
'Alex'      2          4
'Brian'     1          2
'Cathy'     3          5
'Daniel'    5          7

假设我有一些表单代码

for name in df:
    if points > 2:
        grade = 'pass'
    else:
        grade = 'fail'

    average_points = points/attempts
    attempts_left = 10 - attempts

name        grade    average_points    attempts_left
'Alex'      fail          0.5               6
'Brian'     fail          0.5               8
'Cathy'     pass          0.6               5
'Daniel'    pass          0.71              3

我想在这里实现的是一个输出表（在一个数据框中），形式如下

for name in df:
    if points > 2:
        grade = 'pass'
    else:
        grade = 'fail'

    average_points = points/attempts
    attempts_left = 10 - attempts

name        grade    average_points    attempts_left
'Alex'      fail          0.5               6
'Brian'     fail          0.5               8
'Cathy'     pass          0.6               5
'Daniel'    pass          0.71              3

问题是，我不确定应该在代码中使用哪些返回/附加函数。此外，我知道在我的原始数据集中为“grade”、“average_points”和“attempts_left”添加列可能更简单，但这种方法在我的情况下不起作用，因为我的原始数据比上面的工作示例更复杂

任何帮助都将不胜感激。谢谢

使用

pandas.DataFrame（）

和

df.append

：

df2 = pandas.DataFrame()
for i,row in df.iterrows():
    points = row["points"]
    attempts = row["attempts"]
    new_row = {}
    new_row["name"] = row["name"]
    if points > 2:
        new_row["grade"] = 'pass'
    else:
        new_row["grade"] = 'fail'

    new_row["average_points"] = points/attempts
    new_row["attempts_left"] = 10 - attempts
    df2 = df2.append(pandas.DataFrame(new_row,index=[i]))
print(df2)

产出：

   attempts_left  average_points grade    name
0              6        0.500000  fail    Alex
1              8        0.500000  fail   Brian
2              5        0.600000  pass   Cathy
3              3        0.714286  pass  Daniel

使用

pandas.DataFrame（）

和

df.append

：

df2 = pandas.DataFrame()
for i,row in df.iterrows():
    points = row["points"]
    attempts = row["attempts"]
    new_row = {}
    new_row["name"] = row["name"]
    if points > 2:
        new_row["grade"] = 'pass'
    else:
        new_row["grade"] = 'fail'

    new_row["average_points"] = points/attempts
    new_row["attempts_left"] = 10 - attempts
    df2 = df2.append(pandas.DataFrame(new_row,index=[i]))
print(df2)

产出：

   attempts_left  average_points grade    name
0              6        0.500000  fail    Alex
1              8        0.500000  fail   Brian
2              5        0.600000  pass   Cathy
3              3        0.714286  pass  Daniel

---

您可以将操作矢量化并使用

assign

In [839]: df.assign(attempts_left=10 - df.attempts,
     ...:           average_points=df.points / df.attempts,
     ...:           grade=np.where(df.points > 2, 'pass', 'fail'))
Out[839]:
       name  points  attempts  attempts_left  average_points grade
0    'Alex'       2         4              6        0.500000  fail
1   'Brian'       1         2              8        0.500000  fail
2   'Cathy'       3         5              5        0.600000  pass
3  'Daniel'       5         7              3        0.714286  pass

---

您可以将操作矢量化并使用

assign

In [839]: df.assign(attempts_left=10 - df.attempts,
     ...:           average_points=df.points / df.attempts,
     ...:           grade=np.where(df.points > 2, 'pass', 'fail'))
Out[839]:
       name  points  attempts  attempts_left  average_points grade
0    'Alex'       2         4              6        0.500000  fail
1   'Brian'       1         2              8        0.500000  fail
2   'Cathy'       3         5              5        0.600000  pass
3  'Daniel'       5         7              3        0.714286  pass

---

使用

应用

：

import pandas as pd

df = pd.DataFrame([
    ['Alex', 2, 4],
    ['Brian', 1, 2],
    ['Cathy', 3, 5],
    ['Daniel', 5, 7],
], columns=['name', 'points', 'attempts'])

df['grade'] = df['points'].apply(lambda points: 'pass' if points > 2 else 'fail')
df['attempts_left'] = df['points'].apply(lambda points: 'pass' if points > 2 else 'fail')
df['average_points'] = df[['points', 'attempts']].apply(lambda row: row['points']/row['attempts'], axis=1)

new_df = df[['name', 'grade', 'average_points', 'attempts_left']]

---

使用

应用

：

import pandas as pd

df = pd.DataFrame([
    ['Alex', 2, 4],
    ['Brian', 1, 2],
    ['Cathy', 3, 5],
    ['Daniel', 5, 7],
], columns=['name', 'points', 'attempts'])

df['grade'] = df['points'].apply(lambda points: 'pass' if points > 2 else 'fail')
df['attempts_left'] = df['points'].apply(lambda points: 'pass' if points > 2 else 'fail')
df['average_points'] = df[['points', 'attempts']].apply(lambda row: row['points']/row['attempts'], axis=1)

new_df = df[['name', 'grade', 'average_points', 'attempts_left']]

---

谢谢你的帮助，约翰！然而，我正在寻找与df2类似的东西。将（'grade'/'average_points'/'truments_left'）附加到一个新的数据帧上，每个名称作为一个单独的行。更多关于我的原始数据：它是一个timeseries索引，用于一个月内一个玩家的所有尝试。所以在本例中，我实际上是在计算玩家的每日结果（分数、平均分、左尝试次数）并输出表格，每行代表一个月中的一天。不确定我是否足够清楚，所以请随时询问更多信息。干杯。谢谢你的帮助，约翰！然而，我正在寻找与df2类似的东西。将（'grade'/'average_points'/'truments_left'）附加到一个新的数据帧上，每个名称作为一个单独的行。更多关于我的原始数据：它是一个timeseries索引，用于一个月内一个玩家的所有尝试。所以在本例中，我实际上是在计算玩家的每日结果（分数、平均分、左尝试次数）并输出表格，每行代表一个月中的一天。不确定我是否足够清楚，所以请随时询问更多信息。干杯