[英]Dataframe fillna conditional based on Index & Column Name
我希望能够在 Dataframe 上使用df.fillna()
function,但根据该特定单元格的索引和列名对其应用条件。
我正在尝试根据以下数据集创建曲棍球队友数据的热图(为下面的大字典道歉)-
linemates_toi = {
'Player 1': {'Player 2': 0.25, 'Player 3': 7.95, 'Player 4': 0.6333, 'Player 5': 9.95, 'Player 6': 0.6333, 'Player 7': 0.8, 'Player 8': 4.2667, 'Player 9': 7.8833, 'Player 10': 0.3, 'Player 11': 11.2333, 'Player 12': 3.35, 'Player 13': 0.2167},
'Player 10': {'Player 14': 2.3, 'Player 18': 1.2667, 'Player 2': 6.8333, 'Player 4': 5.5833, 'Player 5': 0.9, 'Player 16': 6.9167, 'Player 6': 4.9667, 'Player 7': 4.15, 'Player 15': 1.0, 'Player 8': 0.3167, 'Player 17': 5.3167, 'Player 1': 0.3, 'Player 11': 1.6167, 'Player 12': 0.6833, 'Player 13': 12.7167},
'Player 12': {'Player 14': 4.5333, 'Player 18': 4.3333, 'Player 2': 3.1167, 'Player 3': 1.2333, 'Player 4': 5.7333, 'Player 5': 3.5167, 'Player 16': 3.0, 'Player 6': 3.0167, 'Player 7': 2.4, 'Player 15': 2.0167, 'Player 8': 11.6667, 'Player 17': 2.2667, 'Player 9': 0.1167, 'Player 1': 3.35, 'Player 10': 0.6833, 'Player 11': 3.35},
'Player 17': {'Player 14': 4.55, 'Player 18': 1.65, 'Player 2': 0.8833, 'Player 3': 2.85, 'Player 5': 0.0333, 'Player 16': 2.9167, 'Player 6': 7.8167, 'Player 7': 6.0833, 'Player 8': 3.8, 'Player 9': 2.25, 'Player 10': 5.3167, 'Player 12': 2.2667, 'Player 13': 5.7833},
'Player 7': {'Player 18': 0.3667, 'Player 2': 0.6667, 'Player 3': 1.55, 'Player 4': 0.3333, 'Player 5': 0.15, 'Player 16': 1.2167, 'Player 6': 6.8333, 'Player 15': 0.3333, 'Player 8': 3.0667, 'Player 17': 6.0833, 'Player 9': 1.8833, 'Player 1': 0.8, 'Player 10': 4.15, 'Player 11': 1.0, 'Player 12': 2.4, 'Player 13': 4.4333},
'Player 16': {'Player 14': 2.2833, 'Player 2': 8.5333, 'Player 3': 2.7, 'Player 4': 8.0167, 'Player 5': 0.45, 'Player 6': 0.4, 'Player 7': 1.2167, 'Player 8': 2.3, 'Player 17': 2.9167, 'Player 9': 2.15, 'Player 10': 6.9167, 'Player 11': 0.1333, 'Player 12': 3.0, 'Player 13': 6.5833},
'Player 18': {'Player 14': 10.05, 'Player 2': 0.75, 'Player 3': 5.0, 'Player 4': 3.45, 'Player 5': 0.3333, 'Player 6': 0.8333, 'Player 7': 0.3667, 'Player 15': 5.2, 'Player 8': 5.8167, 'Player 17': 1.65, 'Player 9': 4.3833, 'Player 10': 1.2667, 'Player 11': 1.5, 'Player 12': 4.3333, 'Player 13': 1.5333},
'Player 13': {'Player 14': 3.0333, 'Player 18': 1.5333, 'Player 2': 5.9167, 'Player 3': 0.7333, 'Player 4': 4.95, 'Player 5': 0.8167, 'Player 16': 6.5833, 'Player 6': 5.1333, 'Player 7': 4.4333, 'Player 15': 1.2667, 'Player 8': 0.2833, 'Player 17': 5.7833, 'Player 1': 0.2167, 'Player 10': 12.7167, 'Player 11': 1.5333},
'Player 5': {'Player 18': 0.3333, 'Player 2': 0.8333, 'Player 3': 8.0333, 'Player 16': 0.45, 'Player 6': 0.3333, 'Player 7': 0.15, 'Player 8': 3.0167, 'Player 17': 0.0333, 'Player 9': 6.7333, 'Player 1': 9.95, 'Player 10': 0.9, 'Player 11': 11.2333, 'Player 12': 3.5167, 'Player 13': 0.8167},
'Player 15': {'Player 14': 4.5667, 'Player 18': 5.2, 'Player 2': 0.4667, 'Player 3': 2.35, 'Player 6': 0.1667, 'Player 7': 0.3333, 'Player 8': 2.0167, 'Player 9': 2.0833, 'Player 10': 1.0, 'Player 12': 2.0167, 'Player 13': 1.2667},
'Player 2': {'Player 18': 0.75, 'Player 3': 2.65, 'Player 4': 8.6, 'Player 5': 0.8333, 'Player 16': 8.5333, 'Player 6': 0.8333, 'Player 7': 0.6667, 'Player 15': 0.4667, 'Player 8': 2.3333, 'Player 17': 0.8833, 'Player 9': 1.9167, 'Player 1': 0.25, 'Player 10': 6.8333, 'Player 11': 1.6167, 'Player 12': 3.1167, 'Player 13': 5.9167},
'Player 8': {'Player 14': 5.8333, 'Player 18': 5.8167, 'Player 2': 2.3333, 'Player 3': 1.1167, 'Player 4': 5.6833, 'Player 5': 3.0167, 'Player 16': 2.3, 'Player 6': 4.2667, 'Player 7': 3.0667, 'Player 15': 2.0167, 'Player 17': 3.8, 'Player 9': 1.1333, 'Player 1': 4.2667, 'Player 10': 0.3167, 'Player 11': 3.8167, 'Player 12': 11.6667, 'Player 13': 0.2833},
'Player 4': {'Player 14': 3.2833, 'Player 18': 3.45, 'Player 2': 8.6, 'Player 3': 2.0667, 'Player 16': 8.0167, 'Player 6': 0.8333, 'Player 7': 0.3333, 'Player 8': 5.6833, 'Player 9': 1.85, 'Player 1': 0.6333, 'Player 10': 5.5833, 'Player 11': 0.85, 'Player 12': 5.7333, 'Player 13': 4.95},
'Player 9': {'Player 14': 4.5167, 'Player 18': 4.3833, 'Player 2': 1.9167, 'Player 3': 14.35, 'Player 4': 1.85, 'Player 5': 6.7333, 'Player 16': 2.15, 'Player 6': 0.8833, 'Player 7': 1.8833, 'Player 15': 2.0833, 'Player 8': 1.1333, 'Player 17': 2.25, 'Player 1': 7.8833, 'Player 11': 9.0667, 'Player 12': 0.1167},
'Player 14': {'Player 18': 10.05, 'Player 3': 5.7167, 'Player 4': 3.2833, 'Player 16': 2.2833, 'Player 6': 1.8833, 'Player 15': 4.5667, 'Player 8': 5.8333, 'Player 17': 4.55, 'Player 9': 4.5167, 'Player 10': 2.3, 'Player 11': 0.9833, 'Player 12': 4.5333, 'Player 13': 3.0333},
'Player 11': {'Player 14': 0.9833, 'Player 18': 1.5, 'Player 2': 1.6167, 'Player 3': 9.7667, 'Player 4': 0.85, 'Player 5': 11.2333, 'Player 16': 0.1333, 'Player 6': 0.5, 'Player 7': 1.0, 'Player 8': 3.8167, 'Player 9': 9.0667, 'Player 1': 11.2333, 'Player 10': 1.6167, 'Player 12': 3.35, 'Player 13': 1.5333},
'Player 6': {'Player 14': 1.8833, 'Player 18': 0.8333, 'Player 2': 0.8333, 'Player 3': 1.1333, 'Player 4': 0.8333, 'Player 5': 0.3333, 'Player 16': 0.4, 'Player 7': 6.8333, 'Player 15': 0.1667, 'Player 8': 4.2667, 'Player 17': 7.8167, 'Player 9': 0.8833, 'Player 1': 0.6333, 'Player 10': 4.9667, 'Player 11': 0.5, 'Player 12': 3.0167, 'Player 13': 5.1333},
'Player 3': {'Player 14': 5.7167, 'Player 18': 5.0, 'Player 2': 2.65, 'Player 4': 2.0667, 'Player 5': 8.0333, 'Player 16': 2.7, 'Player 6': 1.1333, 'Player 7': 1.55, 'Player 15': 2.35, 'Player 8': 1.1167, 'Player 17': 2.85, 'Player 9': 14.35, 'Player 1': 7.95, 'Player 11': 9.7667, 'Player 12': 1.2333, 'Player 13': 0.7333}
}
df = pd.DataFrame(linemates_toi)
我现在要实现的是使用df.fillna(0)
并应用条件,因此唯一被替换的NaN
是索引和列名称不匹配时,因为我希望这些单元格保持NaN
以便当我plot 将它们放入热图中,它们在从 Matplotlib 应用的cmap
中没有任何颜色。
如果我正在编写伪代码,它看起来像这样 -
df.fillna(0, df.cell.Index.Name != df.cell.Column.Name)
提前致谢!
使用一些广播和NaN
-masking
mask = df.index.to_numpy() == df.columns.to_numpy()[:, None]
df.fillna(0).mask(mask)
>>> df.head()
Player 1 Player 10 Player 12 Player 17 Player 7 Player 16 \
Player 1 NaN 0.3000 3.3500 0.0000 0.8000 0.0000
Player 10 0.3000 NaN 0.6833 5.3167 4.1500 6.9167
Player 11 11.2333 1.6167 3.3500 0.0000 1.0000 0.1333
Player 12 3.3500 0.6833 NaN 2.2667 2.4000 3.0000
Player 13 0.2167 12.7167 0.0000 5.7833 4.4333 6.5833
Player 18 Player 13 Player 5 Player 15 Player 2 Player 8 \
Player 1 0.0000 0.2167 9.9500 0.0000 0.2500 4.2667
Player 10 1.2667 12.7167 0.9000 1.0000 6.8333 0.3167
Player 11 1.5000 1.5333 11.2333 0.0000 1.6167 3.8167
Player 12 4.3333 0.0000 3.5167 2.0167 3.1167 11.6667
Player 13 1.5333 NaN 0.8167 1.2667 5.9167 0.2833
Player 4 Player 9 Player 14 Player 11 Player 6 Player 3
Player 1 0.6333 7.8833 0.0000 11.2333 0.6333 7.9500
Player 10 5.5833 0.0000 2.3000 1.6167 4.9667 0.0000
Player 11 0.8500 9.0667 0.9833 NaN 0.5000 9.7667
Player 12 5.7333 0.1167 4.5333 3.3500 3.0167 1.2333
Player 13 4.9500 0.0000 3.0333 1.5333 5.1333 0.7333
您也可以执行以下操作:
df.fillna(0, inplace=True)
for col in df:
df.loc[col, col] = np.nan
解释:
在每列上使用df.apply
到 map 和 lambda :
df = df.apply(lambda col: col.where((col.name == col.index) | col.notnull(), 0))
col.where(condition, value_if_false)
如果condition
为真,则返回col
中的原始值。 否则返回value_if_false
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.