python中的R等价代码。如何将R代码行转换为python？

Question

我有下面这行。如何为R代码编写等价的python代码

c1 <- c(7.15,7.45,8.15,8.45,9.15,9.45,10.15,10.45,11.15,11.45,12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15,18.45,19.15,19.45,20.15)
numeric_vector <- c(12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15)

data <- data.frame(matrix(nrow = 1,ncol = length(c1)))
colnames(data) <- c(c1)
data[1,] <- 0
data[1,colnames(data)[(colnames(data) %in% as.character(numeric_Vector))]] = data[1,colnames(data)[(colnames(data) %in% as.character(numeric_Vector))]] + 1
df <- tibble::rownames_to_column(data.frame(t(data)), "col1")

我在python中尝试了以下方法：

data = pd.DataFrame(index=np.arange(0), columns=np.arange(len(c1)))
data.columns = c1
data[0,] = 0
d1 = pd.DataFrame(numeric_vector)
d1.columns = ['col1']
d1['count'] =d1.apply(lambda x: 1, axis=1)
d1['col1'] = d1['col1'].astype('category')
add_col1 = set(c1) - set(d1['col1'].unique())
d1['col1'] = d1['col1'].cat.add_categories(add_col1)
otData = d1['col1'].value_counts().reset_index()

请帮我把这些行转换成python。它给出了不同的输出

Answer 1

R:

df <- data.frame(col1=c1)
df$col2 <- as.integer(d$col1 %in% numeric_vector)

Python:

import pandas as pd
df = pd.DataFrame({'col1': c1})
df['col2'] = df.col1.isin(numeric_vector).astype(int)

---

比较输出：

首先，在R中：

c1 <- c(7.15,7.45,8.15,8.45,9.15,9.45,10.15,10.45,11.15,11.45,12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15,18.45,19.15,19.45,20.15)
numeric_vector = c(12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15)

df <- data.frame(col1=c1)
df$col2 <- as.integer(df$col1 %in% numeric_vector)
write.csv(df, 'df.csv', row.names = F)

然后，在Python中：

c1 = [7.15,7.45,8.15,8.45,9.15,9.45,10.15,10.45,11.15,11.45,12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15,18.45,19.15,19.45,20.15]
numeric_vector = [12.15,12.45,13.15,13.45,14.15,14.45,15.15,15.45,16.15,16.45,17.15,17.45,18.15]

import pandas as pd
df = pd.DataFrame({'col1': c1})
df['col2'] = df.col1.isin(numeric_vector).astype(int)

# Compare if all values are equal
df_R = pd.read_csv('df.csv')
print((df_R == df).values.all())
True

# Merge and compare outputs:
print(df.add_suffix('_Python').join(df_R.add_suffix('_R')))

    col1_Python  col2_Python  col1_R  col2_R
0          7.15            0    7.15       0
1          7.45            0    7.45       0
2          8.15            0    8.15       0
3          8.45            0    8.45       0
4          9.15            0    9.15       0
5          9.45            0    9.45       0
6         10.15            0   10.15       0
7         10.45            0   10.45       0
8         11.15            0   11.15       0
9         11.45            0   11.45       0
10        12.15            1   12.15       1
11        12.45            1   12.45       1
12        13.15            1   13.15       1
13        13.45            1   13.45       1
14        14.15            1   14.15       1
15        14.45            1   14.45       1
16        15.15            1   15.15       1
17        15.45            1   15.45       1
18        16.15            1   16.15       1
19        16.45            1   16.45       1
20        17.15            1   17.15       1
21        17.45            1   17.45       1
22        18.15            1   18.15       1
23        18.45            0   18.45       0
24        19.15            0   19.15       0
25        19.45            0   19.45       0
26        20.15            0   20.15       0