英文:
Creating new column with values that depends on other columns
问题
以下是您的代码的翻译部分：
这是我的数据框架
import pandas as pd
url = 'https://www.basketball-reference.com/boxscores/pbp/200911060GSW.html'
dfs = pd.read_html(url)
df = dfs[0]
df.columns = df.columns.droplevel() # 删除数据框架的“1st Q”多级标题
df.rename(columns={'Unnamed: 2_level_1': 'PM1', 'Unnamed: 4_level_1': 'PM2'}, inplace=True)
然后，我对库里的数据进行了子集化，因为我关注他的动作。
df_curry = df.loc[df["Golden State"].str.contains("Curry", na=False)]
df_curry`
现在我尝试将投篮命中和未命中的投篮添加到一个新列中，以便稍后计算命中率，但我总是得到错误消息“str' object has no attribute 'str'”。也许有人可以帮助我或给我另一种方法。
# 计算命中率
field_throws_missed = 0
field_throws_hit = 0`
# 创建新列
df_curry["Field Goals Hit"] = 0
df_curry["Field Goals Missed"] = 0
df_curry["Field Goals Percentage"] = 0`
for row in range(len(df_curry["Golden State"])):
if df_curry.iloc[row]["Golden State"].str.contains("misses 2|misses 3"):
field_throws_missed += 1
df_curry.iloc[row]["Field Goals Missed"] = field_throws_missed
elif df_curry.iloc[row]["Golden State"].str.contains("makes 2|makes 3"):
field_throws_hit += 1
df_curry.iloc[row]["Field Goals Hit"] = field_throws_hit`
希望这能帮助您理解您的代码。如果您有其他问题，请随时提出。
英文:
This is my dataframe
import pandas as pd
url = 'https://www.basketball-reference.com/boxscores/pbp/200911060GSW.html'
dfs = pd.read_html(url)
df = dfs[0]
df.columns = df.columns.droplevel() # drops the "1st Q" Multilevel header of the dataframe
df.rename(columns={'Unnamed: 2_level_1': 'PM1', 'Unnamed: 4_level_1': 'PM2'}, inplace=True)
then i have made a subset of curry because I focus on his actions.
df_curry = df.loc[df["Golden State"].str.contains("Curry", na=False)]
df_curry`
now i tried to insert the hit and not hit throws into a new column to calculate the quote later but i always get the error "str' object has no attribute 'str'.
Maybe someone can help me or give me another approach
# Calculating Hit Rate
field_throws_missed = 0
field_throws_hit = 0`
# Creating the new Columns
df_curry["Field Goals Hit"] = 0
df_curry["Field Goals Missed"] = 0
df_curry["Field Goals Percentage"] = 0`
for row in range(len(df_curry["Golden State"])):
if df_curry.iloc[row]["Golden State"].str.contains("misses 2|misses 3"):
field_throws_missed += 1
df_curry.iloc[row]["Field Goals Missed"] = field_throws_missed
elif df_curry.iloc[row]["Golden State"].str.contains("makes 2|makes 3"):
field_throws_hit += 1
df_curry.iloc[row]["Field Goals Hit"] = field_throws_hit`
答案1
得分: 0
不需要循环，要计算True值的数量，请使用Series.cumsum进行累积求和：
df_curry = df.loc[df["Golden State"].str.contains("Curry", na=False)].copy()
df_curry["Field Goals Hit"] = df_curry["Golden State"].str.contains("misses 2|misses 3").cumsum()
df_curry["Field Goals Missed"] = df_curry["Golden State"].str.contains("makes 2|makes 3").cumsum()
编辑：如果需要在下一行添加1，请使用以下代码：
df_curry["Field Goals Hit"] = df_curry["Golden State"].str.contains("misses 2|misses 3").shift(fill_value=0).cumsum()
df_curry["Field Goals Missed"] = df_curry["Golden State"].str.contains("makes 2|makes 3").shift(fill_value=0).cumsum()
英文:
No loops necessary here, for count Trues values use cumulative sum by Series.cumsum:
df_curry = df.loc[df["Golden State"].str.contains("Curry", na=False)].copy()
df_curry["Field Goals Hit"] = df_curry["Golden State"].str.contains("misses 2|misses 3").cumsum()
df_curry["Field Goals Missed"] = df_curry["Golden State"].str.contains("makes 2|makes 3").cumsum()
EDIT: If need add 1 in next row use:
df_curry["Field Goals Hit"] = df_curry["Golden State"].str.contains("misses 2|misses 3").shift(fill_value=0).cumsum()
df_curry["Field Goals Missed"] = df_curry["Golden State"].str.contains("makes 2|makes 3").shift(fill_value=0).cumsum()
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