I keep getting the following error.
I read a file that contains time series data of 3 columns: [meter ID] [daycode(explain later)] [meter reading in kWh]
consum = pd.read_csv("data/File1.txt", delim_whitespace=True, encoding = "utf-8", names =['meter', 'daycode', 'val'], engine='python')
consum.set_index('meter', inplace=True)
test = consum.loc[[1048]]
I will observe meter readings for all the length of data that I have in this file, but first filter by meter ID.
test['day'] = test['daycode'].astype(str).str[:3]
test['hm'] = test['daycode'].astype(str).str[-2:]
For readability, I convert daycode based on its rule. First 3 digits are in range of 1 to 365 x2 = 730, last 2 digits in range of 1 to 48. These are 30-min interval reading of 2-year length. (but not all have in full)
So I create files that contain dates in one, and times in another separately. I will use index to convert the digits of daycode into the corresponding date & time that these file contain.
#dcodebook index starts from 0. So minus 1 from the daycode before match
dcodebook = pd.read_csv("data/dcode.txt", encoding = "utf-8", sep = '\r', names =['match'])
#hcodebook starts from 1
hcodebook = pd.read_csv("data/hcode.txt", encoding = "utf-8", sep ='\t', lineterminator='\r', names =['code', 'print'])
hcodebook = hcodebook.drop(['code'], axis= 1)
For some weird reason, dcodebook was indexed using .iloc function as I understood, but hcodebook needed .loc.
#iloc: by int-position
#loc: by label value
#ix: by both
day_df = dcodebook.iloc[test['day'].astype(int) - 1].reset_index(drop=True)
#to avoid duplicate index Valueerror, create separate dataframes..
hm_df = hcodebook.loc[test['hm'].astype(int) - 1]
#.to_frame error / do I need .reset_index(drop=True)?
The following line is where the code crashes.
datcode_df = day_df(['match']) + ' ' + hm_df(['print'])
print datcode_df
print test
What I don't understand:
I tested earlier that columns of different dataframes can be merged using the simple addition as seen
I initially assigned this to the existing column ['daycode'] in test dataframe, so that previous values will be replaced. And the same error msg was returned.
Please advise.
You need same size of both DataFrames, so is necessary day and hm are unique.
Then reset_index with drop=True for same indices and last remove () in join:
day_df = dcodebook.iloc[test['day'].astype(int) - 1].reset_index(drop=True)
hm_df = hcodebook.loc[test['hm'].astype(int) - 1].reset_index(drop=True)
datcode_df = day_df['match'] + ' ' + hm_df['print']
Related
I have a dataframe (all5) including one column with dates('CREATIE_DATUM'). Sometimes the notation is 01/JAN/2015 sometimes it's written as 01-JAN-15.
I only need the year, so I wrote the following code line:
all5[['Day','Month','Year']]=all5['CREATIE_DATUM'].str.split('-/',expand=True)
but I get the following error:
columns must be same length as key
so I assume somewhere in my dataframe (>100.000 lines) a value has more than two '/' signs.
How can I make my code skip this line?
You can try to use pd.to_datetime and then use .dt property to access day, month and year:
x = pd.to_datetime(all5["CREATIE_DATUM"])
all5["Day"] = x.dt.day
all5["Month"] = x.dt.month
all5["Year"] = x.dt.year
I want to combine the CSV files from the Johns Hopkins Covid Data (e.g. https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/05-10-2020.csv & https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-23-2020.csv).
I already managed to load the files into a DataFrame as well as sanitizing the header (_ vs. / in some names). Now I want to pick one column (e.g. Confirmed), rename it to the day of the file and then combine those CSV files to get a progress over time.
This merge needs to be done by state_province. In both frames, the key may not be present. How can I do this? I experimented with rightjoin and outerjoin, but didn't have any success. Can someone point me the right way please?
I initially didn't want to share the code that I have so far because I didn't want to guide to a specific solution - but here it is. It is copied together from several Jupyter cells.
using Dates
start = Dates.Date(2020,1,22) #begin of recording
now = Dates.Date(Dates.now())- Dates.Day(1) #today
date_range = collect(start:Dates.Day(1):now) #create a date range with 1 element per day
prefix = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/"
suffix = ".csv"
function create_url(date)
return prefix * Dates.format(date, "mm-dd-YYYY") * suffix
end
function cleanup_column_names(name)
if name == "Country/Region" || name == "Country_Region"
return "country"
elseif name == "Province/State" || name == "Province_State"
return "state"
else
return name
end
end
using CSV
using HTTP
using DataFrames
selected_data = "Confirmed"
date = date_range[1]
data = DataFrame(CSV.File(HTTP.get(create_url(date)).body))
DataFrames.rename!(cleanup_column_names, data)
DataFrames.select!(data,["state", "country", selected_data])
DataFrames.rename!(data, 3 => Dates.format(date, "YYYY-mm-dd"))
Regards
Tobias
I am relatively new to Julia, so take my answer with a bit of scepticism:
First, we wrap the DataFrame creation into a function:
function prepare_date_df(date)
data = DataFrame(CSV.File(HTTP.get(create_url(date)).body))
DataFrames.rename!(cleanup_column_names, data)
DataFrames.select!(data,["state", "country", selected_data])
DataFrames.rename!(data, 3 => Dates.format(date, "YYYY-mm-dd"))
return data
end
Let's create our first Dataframe:
df = prepare_date_df(date_range[1])
Now, let's iterate over all the other dates, create a dataframe for each date and merge this with our first dataframe:
for date in date_range[2:end]
df_new = prepare_date_df(date)
df = outerjoin(df, df_new, on = [:state, :country])
end
This works fine for the first two months, but with the growing Dataframes, it suddenly gets very slow (and even hangs?). So I would be very interested in a more performative answer!
I have a dataset that indicates date & time in 5-digit format: ddd + hm
ddd part starts from 2009 Jan 1. Since the data was collected only from then to 2-years period, its [min, max] would be [1, 365 x 2 = 730].
Data is observed in 30-min interval, making 24 hrs per day period to lengthen to 48 at max. So [min, max] for hm at [1, 48].
Following is the excerpt of daycode.csv file that contains ddd part of the daycode, matching date & hm part of the daycode, matching time.
And I think I agreed to not showing the dataset which is from ISSDA. So..I will just describe that the daycode in the File1.txt file reads like '63317'.
This link gave me a glimpse of how to approach this problem, and I was in the middle of putting up this code together..which of course won't work at this point.
consume = pd.read_csv("data/File1.txt", sep= ' ', encoding = "utf-8", names =['meter', 'daycode', 'val'])
df1= pd.read_csv("data/daycode.csv", encoding = "cp1252", names =['code', 'print'])
test = consume[consume['meter']==1048]
test['daycode'] = test['daycode'].map(df1.set_index('code')['print'])
plt.plot(test['daycode'], test['val'], '.')
plt.title('test of meter 1048')
plt.xlabel('daycode')
plt.ylabel('energy consumption [kWh]')
plt.show()
Not all units(thousands) have been observed at full length but 730 x 48 is a large combination to lay out on excel by hand. Tbh, not an elegant solution but I tried by dragging - it doesn't quite get it.
If I could read the first 3 digits of the column values and match with another file's column, 2 last digits with another column, then combine.. is there a way?
For the last 2 lines you can just do something like this
df['first_3_digits'] = df['col1'].map(lambda x: str(x)[:3])
df['last_2_digits'] = df['col1'].map(lambda x: str(x)[-2:])
for joining 2 dataframes
df3 = df.merge(df2,left_on=['first_3_digits','last_2_digits'],right_on=['col1_df2','col2_df2'],how='left')
I have a data in the following form:
product/productId B000EVS4TY
1 product/title Arrowhead Mills Cookie Mix, Chocolate Chip, 1...
2 product/price unknown
3 review/userId A2SRVDDDOQ8QJL
4 review/profileName MJ23447
5 review/helpfulness 2/4
6 review/score 4.0
7 review/time 1206576000
8 review/summary Delicious cookie mix
9 review/text I thought it was funny that I bought this pro...
10 product/productId B0000DF3IX
11 product/title Paprika Hungarian Sweet
12 product/price unknown
13 review/userId A244MHL2UN2EYL
14 review/profileName P. J. Whiting "book cook"
15 review/helpfulness 0/0
16 review/score 5.0
17 review/time 1127088000
I want to convert it to a dataframe such that the entries in the 1st column
product/productId
product/title
product/price
review/userId
review/profileName
review/helpfulness
review/score
review/time
review/summary
review/text
are the column headers with the values arranged corresponding to each header in the table.
I still had a tiny doubt about your file, but since both my suggestions are quite similar, I will try to address both the scenarios you might have.
In case your file doesn't actually have the line numbers inside of it, this should do it:
filepath = "./untitled.txt" # you need to change this to your file path
column_separator="\s{3,}" # we'll use a regex, I explain some caveats of this below...
# engine='python' surpresses a warning by pandas
# header=None is that so all lines are considered 'data'
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None)
df = df.set_index(0) # this takes column '0' and uses it as the dataframe index
df = df.T # this makes the data look like you were asking (goes from multiple rows+1column to multiple columns+1 row)
df = df.reset_index(drop=True) # this is just so the first row starts at index '0' instead of '1'
# you could just do the last 3 lines with:
# df = df.set_index(0).T.reset_index(drop=True)
If you do have line numbers, then we just need to do some little adjustments
filepath = "./untitled1.txt"
column_separator="\s{3,}"
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None, index_col=0)
df.set_index(1).T.reset_index(drop=True) #I did all the 3 steps in 1 line, for brevity
In this last case, I would advise you change it in order to have line numbers in all of them (in the example you provided, the numbering starts at the second line, this might be an option about how you handle headers when exporting the data in whatever tool you might be using
Regarding the regex, the caveat is that "\s{3,}" looks for any block of 3 consecutive whitespaces or more to determine the column separator. The problem here is that we'll depend a bit on the data to find the columns. For instance, if in any of the values just so happens to appear 3 consecutive spaces, pandas will raise an exception, since the line will have one more column than the others. One solution to this could be increasing it to any other 'appropriate' number, but then we still depend on the data (for instance, with more than 3, in your example, "review/text" would have enough spaces for the two columns to be identified)
edit after realising what you meant by "stacked"
Whatever "line-number scenario" you have, you'll need to make sure you always have the same number of columns for all registers and reshape the continuous dataframe with something similar to this:
number_of_columns = 10 # you'll need to make sure all "registers" do have the same number of columns otherwise this will break
new_shape = (-1,number_of_columns) # this tuple will mean "whatever number of lines", by 10 columns
final_df = pd.DataFrame(data = df.values.reshape(new_shape)
,columns=df.columns.tolist()[:-10])
Again, take notice of making sure that all lines have the same number of columns (for instance, a file with just the data you provided, assuming 10 columns, wouldn't work). Also, this solution assumes all columns will have the same name.
Currently the index in my data frame has the default values of 0,1,2 .. n-1 where n is the number of rows in the dataframe.
Is there a simple way to change the index values to 0,,1,,2,, ... n-1, where a comma is appended to each index value. So 0 becomes 0, and 1 becomes 1, and so on.
I'd keep it simple.
d.index = d.index.to_series().astype(str) + ','
I converted the existing index to a series so that I could conveniently add a comma to it. However I had to ensure it was of type str before I did so.
Sure, see below:
d = pd.DataFrame(...)
d.index = [str(i)+',' for i in d.index]
But what are you trying to do with this? It seems odd to modify the index like this. If you're trying to print the the data frame in a special format or something else there is probably a better way.
For custom output, you could do something like
for i, row in d.iterrows():
print i + ': ' + ', '.join(row)