How to create a dataframe based on list containing column names?-CodePudding

How to create a dataframe based on list containing column names?

Situation

I've got a list of column names stored in variable named data:

values_c1_114
values_c1_84
values_c1_37
values_c1_126 ...

In total there are 552 elements in the list data.

Now i want to create a dataframe based on this list of column names.

I thought i could access the values behind the column names by using:

for element in data:
    print(element)

But this coding only returns the column names and not the values behind the named column.

Result using for element approach

It is possible to access the values behind the column names.

Accessing single column values

SOLUTION The following approach solved my problem. The "columns" contains the list of the column names that should be added to the dataframe.

data = {k: eval(k) for k in columns} df = pd.DataFrame(data) print(df)

CodePudding user response：

See if this is what you need. If I understand OP's question right, OP's key problem is how to get the variable name as a string, then use the set of string as the dataframe column

def namestr(obj, namespace):
    return [name for name in namespace if namespace[name] is obj][0]

import pandas as pd

# to simulate the data you have
col1 = [1, 2, 3]
col2 = [4, 5, 6]
data = [col1, col2]

df = pd.DataFrame(data).T
df.columns = [namestr(i, globals()) for i in data]
print(df)

Output
   col1  col2
0     1     4
1     2     5
2     3     6

Or the other way around, you have column names as strings in a list, then you could do something like this.

columns = ['col1','col2']
col1 = [1, 2, 3]
col2 = [3, 4, 5]

data = {k: eval(k) for k in columns}

df = pd.DataFrame(data)
print(df)

Output
   col1  col2
0     1     3
1     2     4
2     3     5

CodePudding user response：

You need get. Try for example:

variable <- 125
variable.name <- "variable"
get(variable.name)
# 125

So to build a data frame from your list of variable names stored in data you'd do something like

data.values <- lapply(data, get)  # get a list of data values
names(data.values ) <- data  # sets the names of each variable in the list
df <- data.frame(data.values)

CodePudding user response：

# importing library
import pandas as pd

# list of column names
data = ['values_c1_114','values_c1_84','values_c1_37','values_c1_126']

# data inside each columns
values_c1_114_list = [i for i in range(1,11)]
values_c1_84_list = [i for i in range(11,21)]
values_c1_37_list = [i for i in range(21,31)]
values_c1_126_list = [i for i in range(31,41)]


# creating dict 
dict = {
    'values_c1_114':values_c1_114_list,
    'values_c1_84':values_c1_84_list,
    'values_c1_37':values_c1_37_list,
    'values_c1_126':values_c1_126_list
}

# creating dataframe
df = pd.DataFrame(dict)

#printing dataframe
print(df)