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In Pandas, how can I extract certain value using the key off of a dataframe imported from a csv file?

Using Pandas, I'm trying to extract value using the key but I keep failing to do so. Could you help me with this?
There's a csv file like below:
value
"{""id"":""1234"",""currency"":""USD""}"
"{""id"":""5678"",""currency"":""EUR""}"
I imported this file in Pandas and made a DataFrame out of it:
dataframe from a csv file
However, when I tried to extract the value using a key (e.g. df["id"]), I'm facing an error message.
I'd like to see a value 1234 or 5678 using df["id"]. Which step should I take to get it done? This may be a very basic question but I need your help. Thanks.

The csv file isn't being read in correctly.
You haven't set a delimiter; pandas can automatically detect a delimiter but hasn't done so in your case. See the read_csv documentation for more on this. Because the , the pandas dataframe has a single column, value, which has entire lines from your file as individual cells - the first entry is "{""id"":""1234"",""currency"":""USD""}". So, the file doesn't have a column id, and you can't select data by id.
The data aren't formatted as a pandas df, with row titles and columns of data. One option is to read in this data is to manually process each row, though there may be slicker options.
file = 'test.dat'
f = open(file,'r')
id_vals = []
currency = []
for line in f.readlines()[1:]:
## remove obfuscating characters
for c in '"{}\n':
line = line.replace(c,'')
line = line.split(',')
## extract values to two lists
id_vals.append(line[0][3:])
currency.append(line[1][9:])

You just need to clean up the CSV file a little and you are good. Here is every step:
# open your csv and read as a text string
with open('My_CSV.csv', 'r') as f:
my_csv_text = f.read()
# remove problematic strings
find_str = ['{', '}', '"', 'id:', 'currency:','value']
replace_str = ''
for i in find_str:
my_csv_text = re.sub(i, replace_str, my_csv_text)
# Create new csv file and save cleaned text
new_csv_path = './my_new_csv.csv' # or whatever path and name you want
with open(new_csv_path, 'w') as f:
f.write(my_csv_text)
# Create pandas dataframe
df = pd.read_csv('my_new_csv.csv', sep=',', names=['ID', 'Currency'])
print(df)
Output df:
ID Currency
0 1234 USD
1 5678 EUR

You need to extract each row of your dataframe using json.loads() or eval()
something like this:
import json
for row in df.iteritems():
print(json.loads(row.value)["id"])
# OR
print(eval(row.value)["id"])

how to read data (using pandas?) so that it is correctly formatted?

I have a txt file with following format:
{"results":[{"statement_id":0,"series":[{"name":"datalogger","columns":["time","ActivePower0","CosPhi0","CurrentRms0","DcAnalog5","FiringAngle0","IrTemperature0","Lage 1_Angle1","Lage 1_Angle2","PotentioMeter0","Rotation0","SNR","TNR","Temperature0","Temperature3","Temperature_MAX31855_0","Temperature_MAX31855_3","Vibra0_X","Vibra0_Y","Vibra0_Z","VoltageAccu0","VoltageRms0"],"values":[["2017-10-06T08:50:25.347Z",null,null,null,null,null,null,null,null,null,null,"41762721","Testcustomer",null,null,null,null,-196,196,-196,null,null],["2017-10-06T08:50:25.348Z",null,null,null,null,null,null,346.2964,76.11179,null,null,"41762721","Testcustomer",null,null,null,null,null,null,null,null,null],["2017-10-06T08:50:25.349Z",null,null,2596,null,null,null,null,null,null,null,"41762721","Testkunde",null,null,null,null,null,null,null,null,80700],["2017-10-06T08:50:25.35Z",null,null,null,null,null,null,null,null,null,1956,"41762721","Testkunde",null,null,null,null,null,null,null,null,null],["2017-10-06T09:20:05.742Z",null,null,null,null,null,67.98999,null,null,null,null,"41762721","Testkunde",null,null,null,null,null,null,null,null,null]]}]}]}
...
So in the text file everything is saved in one line. CSV file is not available.
I would like to have it as a data frame in pandas. when I use read.csv:
df = pd.read_csv('time-series-data.txt', sep = ",")
the output of print(df) is someting like [0 rows x 3455.. columns]
So currently everything is read in as one line. However, I would like to have 22 columns (time, activepower0, CosPhi0,..). I ask for tips, thank you very much.
Is a pandas dataframe even suitable for this? the text files are up to 2 GB in size.

Here's an example which can read the file you posted.
Here's the test file, named test.json:
{"results":[{"statement_id":0,"series":[{"name":"datalogger","columns":["time","ActivePower0","CosPhi0","CurrentRms0","DcAnalog5","FiringAngle0","IrTemperature0","Lage 1_Angle1","Lage 1_Angle2","PotentioMeter0","Rotation0","SNR","TNR","Temperature0","Temperature3","Temperature_MAX31855_0","Temperature_MAX31855_3","Vibra0_X","Vibra0_Y","Vibra0_Z","VoltageAccu0","VoltageRms0"],
"values":[
["2017-10-06T08:50:25.347Z",null,null,null,null,null,null,null,null,null,null,"41762721","Test-customer",null,null,null,null,-196,196,-196,null,null],
["2017-10-06T08:50:25.348Z",null,null,null,null,null,null,346.2964,76.11179,null,null,"41762721","Test-customer",null,null,null,null,null,null,null,null,null]]}]}]}
Here's the python code used to read it in:
import json
import pandas as pd
# Read test file.
# This reads the entire file into memory at once. If this is not
# possible for you, you may want to look into something like ijson:
# https://pypi.org/project/ijson/
with open("test.json", "rb") as f
data = json.load(f)
# Get the first element of results list, and first element of series list
# You may need a loop here, if your real data has more than one of these.
subset = data['results'][0]['series'][0]
values = subset['values']
columns = subset['columns']
df = pd.DataFrame(values, columns=columns)
print(df)

Best way to import an entire .txt. file in a single pandas row

Is there a better way to import a txt file in a single pandas row than the solution below?
import pandas as pd
with open(path_txt_file) as f:
text = f.read().replace("\n", "")
df = pd.DataFrame([text], columns = ["text"])
Sample lines from the .txt file:
Today is a beautiful day.
I will go swimming.
I tried pd.read_csv but it is returning multiple rows due to new lines.

You can concatenate the lines with .str.cat() [pandas-doc]:
text = pd.read_csv(path_txt_file, sep='\n', header=None)[0].str.cat()
df = pd.DataFrame([text], columns=['text'])

Why is the cdc_list getting updated after calling the function read_csv() in total_list?

# Program to combine data from 2 csv file
The cdc_list gets updated after second call of read_csv
overall_list = []
def read_csv(filename):
file_read = open(filename,"r").read()
file_split = file_read.split("\n")
string_list = file_split[1:len(file_split)]
#final_list = []
for item in string_list:
int_fields = []
string_fields = item.split(",")
string_fields = [int(x) for x in string_fields]
int_fields.append(string_fields)
#final_list.append()
overall_list.append(int_fields)
return(overall_list)
cdc_list = read_csv("US_births_1994-2003_CDC_NCHS.csv")
print(len(cdc_list)) #3652
total_list = read_csv("US_births_2000-2014_SSA.csv")
print(len(total_list)) #9131
print(len(cdc_list)) #9131

I don't think the code you pasted explains the issue you've had, at least it's not anywhere I can determine. Seems like there's a lot of code you did not include in what you pasted above, that might be responsible.
However, if all you want to do is merge two csvs (assuming they both have the same columns), you can use Pandas' read_csv and Pandas DataFrame methods append and to_csv, to achieve this with 3 lines of code (not including imports):
import pandas as pd
# Read CSV file into a Pandas DataFrame object
df = pd.read_csv("first.csv")
# Read and append the 2nd CSV file to the same DataFrame object
df = df.append( pd.read_csv("second.csv") )
# Write merged DataFrame object (with both CSV's data) to file
df.to_csv("merged.csv")

How to extract a single row from multiple CSV files to a new file

I have hundreds of CSV files on my disk, and one file added daily and I want to extract one row from each of them and put them in a new file. Then I want to daily add values to that same file. CSV files looks like this:
business_day,commodity,total,delivery,total_lots
.
.
20160831,CTC,,201710,10
20160831,CTC,,201711,10
20160831,CTC,,201712,10
20160831,CTC,Total,,385
20160831,HTC,,201701,30
20160831,HTC,,201702,30
.
.
I want to fetch the row that contains 'Total' from each file. The new file should look like:
business_day,commodity,total,total_lots
20160831,CTC,Total,385
20160901,CTC,Total,555
.
.
The raw files on my disk are named '20160831_foo.CSV', '20160901_foo.CSV etc..
After Googling this I have yet not seen any examples on how to extract only one value from a CSV file. Any hints/help much appreciated. Happy to use pandas if that makes life easier.

I ended up with the following:
import pandas as pd
import glob
list_ = []
filenames = glob.glob('c:\\Financial Data\\*_DAILY.csv')
for filename in filenames:
df = pd.read_csv(filename, index_col = None, usecols = ['business_day', 'commodity', 'total', 'total_lots'], parse_dates = ['business_day'], infer_datetime_format = True)
df = df[((df['commodity'] == 'CTC') & (df['total'] == 'Total'))]
list_.append(df)
df = pd.concat(list_, ignore_index = True)
df['total_lots'] = df['total_lots'].astype(int)
df = df.sort_values(['business_day'])
df = df.set_index('business_day')
Then I save it as my required file.

Read the csv files and process them directly like so:
with open('some.csv', newline='') as f:
reader = csv.reader(f)
for row in reader:
# do something here with `row`
break
I would recommend appending rows onto a list after processing for the rows that you desire, and then passing it onto a pandas Dataframe that will simplify your data manipulations a lot.