数据框并在 for 循环中更新新列值
Dataframe and updating a new column value in a for loop
我正在尝试使用方法和 forloop 更新数据框中的值。我将数据帧传递给方法并使用 for 循环来计算我想要放入最后一列的值。
方法在这里
def vwap2(df):
sumTpv = 0.00
sumVolume = 0
dayVwap = 0.00
for i, row in df.iterrows():
#Get all values from each row
#Find typical price
tp = (row['HIGH'] + row['LOW'] + row['CLOSE'] + row['OPEN']) / 4
tpv = tp * row['VOLUME']
sumTpv= sumTpv + tpv
sumVolume = sumVolume + row['VOLUME']
vwap = sumTpv / sumVolume
#Find VWAP
#df.assign(VWAP = vwap)
#row.assign(VWAP = vwap)
#row["VWAP"] = vwap
df.set_value(row, 'VWAP', vwap)
df = df.reindex(row = row)
df[row] = df[row].astype(float)
dayVwap = dayVwap + vwap
print('Day VWAP = ', dayVwap)
print('TPV sum = ', sumTpv)
print('Day Volume = ', sumVolume)
return df
并且在将 df 传递给方法之前，Dataframe 中已经包含了我添加的列。像这样
df["VWAP"] = ""
#do vwap calculation
df = vwap2(df)
但是值要么都一样，要么不写，要么不写。我尝试了几件事但没有成功。
更新
这是我正在使用的数据，我每次都从 Google 中提取它：
CLOSE HIGH LOW OPEN VOLUME TP \
2018-05-10 22:30:00 97.3600 97.48 97.3000 97.460 371766 97.86375
1525991460000000000 97.2900 97.38 97.1800 97.350 116164 97.86375
1525991520000000000 97.3100 97.38 97.2700 97.270 68937 97.86375
1525991580000000000 97.3799 97.40 97.3101 97.330 46729 97.86375
1525991640000000000 97.2200 97.39 97.2200 97.365 64823 97.86375
TPV SumTPV SumVol VWAP
2018-05-10 22:30:00 3.722224e+08 1.785290e+09 18291710 97.601027
1525991460000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991520000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991580000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991640000000000 3.722224e+08 1.785290e+09 18291710 97.601027
如您所见，所有计算的内容都是相同的。
这是我现在正在使用的。
def vwap2(df):
sumTpv = 0.00
sumVolume = 0
dayVwap = 0.00
for i, row in df.iterrows():
#Get all values from each row
#Find typical price
tp = (row['HIGH'] + row['LOW'] + row['CLOSE'] + row['OPEN']) / 4
df['TP'] = tp
tpv = tp * row['VOLUME']
df['TPV'] = tpv
sumTpv= sumTpv + tpv
df['SumTPV'] = sumTpv
sumVolume = sumVolume + row['VOLUME']
df['SumVol'] = sumVolume
vwap = sumTpv / sumVolume
#Find VWAP
#row.assign(VWAP = vwap)
#row["VWAP"] = vwap
#df.set_value(row, 'VWAP', vwap)
df["VWAP"] = vwap
dayVwap = dayVwap + vwap
print('Day VWAP = ', dayVwap)
print('TPV sum = ', sumTpv)
print('Day Volume = ', sumVolume)
return df
IIUC，您不需要循环，甚至 apply - 您可以使用直接列分配和 cumsum() 来获得您要查找的内容。
一些示例数据：
import numpy as np
import pandas as pd
N = 20
high = np.random.random(N)
low = np.random.random(N)
close = np.random.random(N)
opening = np.random.random(N)
volume = np.random.random(N)
data = {"HIGH":high, "LOW":low, "CLOSE":close, "OPEN":opening, "VOLUME":volume}
df = pd.DataFrame(data)
df.head()
CLOSE HIGH LOW OPEN VOLUME
0 0.848676 0.260967 0.004188 0.139342 0.931406
1 0.771065 0.356639 0.495715 0.652106 0.988217
2 0.288206 0.567776 0.023687 0.809410 0.134134
3 0.832711 0.508586 0.031569 0.120774 0.891948
4 0.857051 0.391618 0.155635 0.069054 0.628036
直接赋值tp和tpv列，然后应用cumsum得到sumTpv和sumVolume:
df["tp"] = (df['HIGH'] + df['LOW'] + df['CLOSE'] + df['OPEN']) / 4
df["tpv"] = df.tp * df['VOLUME']
df["sumTpv"] = df.tpv.cumsum()
df["sumVolume"] = df.VOLUME.cumsum()
df["vwap"] = df.sumTpv.div(df.sumVolume)
df.head()
CLOSE HIGH LOW OPEN VOLUME tp tpv \
0 0.848676 0.260967 0.004188 0.139342 0.931406 0.313293 0.291803
1 0.771065 0.356639 0.495715 0.652106 0.988217 0.568881 0.562178
2 0.288206 0.567776 0.023687 0.809410 0.134134 0.422270 0.056641
3 0.832711 0.508586 0.031569 0.120774 0.891948 0.373410 0.333063
4 0.857051 0.391618 0.155635 0.069054 0.628036 0.368340 0.231331
sumTpv sumVolume vwap
0 0.291803 0.931406 0.313293
1 0.853982 1.919624 0.444869
2 0.910622 2.053758 0.443393
3 1.243685 2.945706 0.422203
4 1.475016 3.573742 0.412737
更新（根据 OP 评论）：
要将 dayVwap 作为所有 vwap 的总和，请使用 dayVwap = df.vwap.sum().
我正在尝试使用方法和 forloop 更新数据框中的值。我将数据帧传递给方法并使用 for 循环来计算我想要放入最后一列的值。
方法在这里
def vwap2(df):
sumTpv = 0.00
sumVolume = 0
dayVwap = 0.00
for i, row in df.iterrows():
#Get all values from each row
#Find typical price
tp = (row['HIGH'] + row['LOW'] + row['CLOSE'] + row['OPEN']) / 4
tpv = tp * row['VOLUME']
sumTpv= sumTpv + tpv
sumVolume = sumVolume + row['VOLUME']
vwap = sumTpv / sumVolume
#Find VWAP
#df.assign(VWAP = vwap)
#row.assign(VWAP = vwap)
#row["VWAP"] = vwap
df.set_value(row, 'VWAP', vwap)
df = df.reindex(row = row)
df[row] = df[row].astype(float)
dayVwap = dayVwap + vwap
print('Day VWAP = ', dayVwap)
print('TPV sum = ', sumTpv)
print('Day Volume = ', sumVolume)
return df
并且在将 df 传递给方法之前，Dataframe 中已经包含了我添加的列。像这样
df["VWAP"] = ""
#do vwap calculation
df = vwap2(df)
但是值要么都一样，要么不写，要么不写。我尝试了几件事但没有成功。
更新
这是我正在使用的数据，我每次都从 Google 中提取它：
CLOSE HIGH LOW OPEN VOLUME TP \
2018-05-10 22:30:00 97.3600 97.48 97.3000 97.460 371766 97.86375
1525991460000000000 97.2900 97.38 97.1800 97.350 116164 97.86375
1525991520000000000 97.3100 97.38 97.2700 97.270 68937 97.86375
1525991580000000000 97.3799 97.40 97.3101 97.330 46729 97.86375
1525991640000000000 97.2200 97.39 97.2200 97.365 64823 97.86375
TPV SumTPV SumVol VWAP
2018-05-10 22:30:00 3.722224e+08 1.785290e+09 18291710 97.601027
1525991460000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991520000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991580000000000 3.722224e+08 1.785290e+09 18291710 97.601027
1525991640000000000 3.722224e+08 1.785290e+09 18291710 97.601027
如您所见，所有计算的内容都是相同的。
这是我现在正在使用的。
def vwap2(df):
sumTpv = 0.00
sumVolume = 0
dayVwap = 0.00
for i, row in df.iterrows():
#Get all values from each row
#Find typical price
tp = (row['HIGH'] + row['LOW'] + row['CLOSE'] + row['OPEN']) / 4
df['TP'] = tp
tpv = tp * row['VOLUME']
df['TPV'] = tpv
sumTpv= sumTpv + tpv
df['SumTPV'] = sumTpv
sumVolume = sumVolume + row['VOLUME']
df['SumVol'] = sumVolume
vwap = sumTpv / sumVolume
#Find VWAP
#row.assign(VWAP = vwap)
#row["VWAP"] = vwap
#df.set_value(row, 'VWAP', vwap)
df["VWAP"] = vwap
dayVwap = dayVwap + vwap
print('Day VWAP = ', dayVwap)
print('TPV sum = ', sumTpv)
print('Day Volume = ', sumVolume)
return df
IIUC，您不需要循环，甚至 apply - 您可以使用直接列分配和 cumsum() 来获得您要查找的内容。
一些示例数据：
import numpy as np
import pandas as pd
N = 20
high = np.random.random(N)
low = np.random.random(N)
close = np.random.random(N)
opening = np.random.random(N)
volume = np.random.random(N)
data = {"HIGH":high, "LOW":low, "CLOSE":close, "OPEN":opening, "VOLUME":volume}
df = pd.DataFrame(data)
df.head()
CLOSE HIGH LOW OPEN VOLUME
0 0.848676 0.260967 0.004188 0.139342 0.931406
1 0.771065 0.356639 0.495715 0.652106 0.988217
2 0.288206 0.567776 0.023687 0.809410 0.134134
3 0.832711 0.508586 0.031569 0.120774 0.891948
4 0.857051 0.391618 0.155635 0.069054 0.628036
直接赋值tp和tpv列，然后应用cumsum得到sumTpv和sumVolume:
df["tp"] = (df['HIGH'] + df['LOW'] + df['CLOSE'] + df['OPEN']) / 4
df["tpv"] = df.tp * df['VOLUME']
df["sumTpv"] = df.tpv.cumsum()
df["sumVolume"] = df.VOLUME.cumsum()
df["vwap"] = df.sumTpv.div(df.sumVolume)
df.head()
CLOSE HIGH LOW OPEN VOLUME tp tpv \
0 0.848676 0.260967 0.004188 0.139342 0.931406 0.313293 0.291803
1 0.771065 0.356639 0.495715 0.652106 0.988217 0.568881 0.562178
2 0.288206 0.567776 0.023687 0.809410 0.134134 0.422270 0.056641
3 0.832711 0.508586 0.031569 0.120774 0.891948 0.373410 0.333063
4 0.857051 0.391618 0.155635 0.069054 0.628036 0.368340 0.231331
sumTpv sumVolume vwap
0 0.291803 0.931406 0.313293
1 0.853982 1.919624 0.444869
2 0.910622 2.053758 0.443393
3 1.243685 2.945706 0.422203
4 1.475016 3.573742 0.412737
更新（根据 OP 评论）：
要将 dayVwap 作为所有 vwap 的总和，请使用 dayVwap = df.vwap.sum().