I am trying to load some large csv files which appear to have multiple columns and I am struggling with it.
I don't know who design these csv files, but they appear to have event data as well as log data in each csv. At the start of each csv file there is some initial status liens as well
Everything is in a separate rows
The Event data uses 2 columns (Data and Event comment)
The Log data has multiple columns( Date and 20+ columns.
I give an example of the type of data setup below:
Initial; [Status] The Zoo is Closed;
Initial; Status] The Sun is Down;
Initial; [Status] Monkeys ar sleeping;
Time;No._Of_Monkeys;Monkeys_inside;Monkeys_Outside;Number_of_Bananas
06:00; 5;5;0;10
07:00; 5;5;0;10
07:10;[Event] Sun is up
08:00; 5;5;0;10
08:30; [Event] Monkey Doors open and Zoo Opens
09:00; 5;5;0;10
08:30; [Event] Monkey Goes out
09:00; 5;4;1;10
08:30; [Event] Monkey Eats Banana
09:00; 5;4;1;9
08:30; [Event] Monkey Goes out
09:00; 5;5;2;9
Now what I want to do is to put the Log data into one data frame and the Initial and Event data into another.
Now I can read the csv files with csv_reader and go row by row but this is proving very slow, especially when trying to go thorough multiple files and each file containing about 40k rows
Below is code I am using below
csv_files = [f for f in os.listdir('.') if f.endswith('.log')]
for file in csv_files:
# Open the CSV file in read mode
with open(file, 'r') as csv_file:
# Use the csv module to parse the file
csv_reader = csv.reader(csv_file, delimiter=';')
# Loop through the rows of the file
for row in csv_reader:
# If the row has event data
if len(row) == 2:
# Add the row to the Eventlog
EventLog = EventLog.append(pd.Series(row), ignore_index=True)
# If the row is separated by a single separator
elif len(row) > 2:
#First row entered into data log will be the column headers
if DataLog.empty:
DataLog=pd.DataFrame(columns=row)
else:
# Add the row to the single_separator_df DataFrame
DataLog = DataLog.append(pd.Series(row), ignore_index=True)
Is there a better way to do this....preferably faster
IF I use pandas read_csv it seems to only load the Initial data. i.e first 3 lines of my data above.
I can use skip rows to skip down to where the data is and then it will load the rest, but I can't see to figure out how to separate out the event and log data from there
so looking for ideas before i lose what little hair I have left.
If I understood your data format corectly, I would do something like this:
# simply read data as one column data without headers and indexes
df = pd.read_csv("your_file_name.log", header=None, sep=',')
# split values in this column by ; (in each row will be list of values)
tmp_df = df[0].str.split(";")
# delete empty values in the first 3 rows (because we have ; in the end of these rows)
tmp_df = tmp_df.map(lambda x: [y for y in x if y != ''])
# those rows which have 2 values we insert in one dataframe
EventLog = pd.DataFrame(tmp_df[tmp_df.str.len() == 2].to_list())
# other ones we inset in another dataframe (in the first row will be column names)
data_log_tmp = tmp_df[tmp_df.str.len() != 2].to_list()
DataLog = pd.DataFrame(data_log_tmp[1:], columns=data_log_tmp[0])
Here is an example of loading a CSV file, assuming that Monkeys_inside field is always NaN in Event data and assigned in log data, because I used it as a condition to retrieve the event data :
df = pd.read_csv('huge_data.csv', skiprows=3, sep=';')
log_df = df.dropna().reset_index(drop=True)
event_df = df[~df['Monkeys_inside'].notnull()].reset_index(drop=True)
And assuming also that all your CSV file contains those 3 Status lines.
Keep in mind that the dataframe will hold duplicated rows if you have some in your csv files, to remove them, you need just to call the drop_duplicates function and you good :
event_df = event_df.drop_duplicates()
Right now I am trying to read in data which is provided in a messy to read-in format. Here is an example
#Name,
#Comment,""
#ExtComment,""
#Source,
[Data]
1,2
3,4
5,6
#[END_OF_FILE]
When working with one or two of these files, I have manually changed the ['DATA'] header to ['x', 'y'] and am able to read in data just fine by skipping the first few rows and not reading the last line.
However, right now I have 30+ files, split between two different folders and I am trying to figure out the best way to read in the files and change the header of each file from ['DATA'] to ['x', 'y'].
The excel files are in a folder one path lower than the file that is supposed to read them (i.e. folder 1 contains set of code below, and folder 2 contains the excel files, folder 1 contains folder 2)
Here is what I have right now:
#sets - refers to the set containing the name of each file (i.e. [file1, file2])
#df - the dataframe which you are going to store the data in
#dataLabels - the headers you want to search for within the .csv file
#skip - the number of rows you want to skip
#newHeader - what you want to change the column headers to be
#pathName - provide path where files are located
def reader (sets, df, dataLabels, skip, newHeader, pathName):
for i in range(len(sets)):
df_temp = pd.read_csv(glob.glob(pathName+ sets[i]+".csv"), sep=r'\s*,', skiprows = skip, engine = 'python')[:-1]
df_temp.column.value[0] = [newHeader]
for j in range(len(dataLabels)):
df_temp[dataLabels[j]] = pd.to_numeric(df_temp[dataLabels[j]],errors = 'coerce')
df.append(df_temp)
return df
When I run my code, I run into the error:
No columns to parse from file
I am not quite sure why - I have tried skipping past the [DATA] header and I still receive that error.
Note, for this example I would like the headers to be 'x', 'y' - I am trying to make a universal function so that I could change it to something more useful depending on what I am measuring.
If the #[DATA] row is to be replaced regardless, just ignore it. You can just tell pandas to ignore lines that start with # and then specify your own names:
import pandas as pd
df = pd.read_csv('test.csv', comment='#', names=['x', 'y'])
which gives
x y
0 1 2
1 3 4
2 5 6
Expanding Kraigolas's answer, to do this with multiple files you can use a list comprehension:
files = [glob.glob(f"{pathName}{set_num}.csv") for set_num in sets]
df = pd.concat([pd.read_csv(file, comment="#", names = ["x", "y"]) for file in files])
If you're lucky, you can use Kraigolas' answer to treat those lines as comments.
In other cases you may be able to use the skiprows argument to skip header columns:
df= pd.read_csv(path,skiprows=10,skipfooter=2,names=["x","y"])
And yes, I do have an unfortunate file with a 10-row heading and 2 rows of totals.
Unfortunately I also have very unfortunate files where the number of headings change.
In this case I used the following code to iterate until I find the first "good" row, then create a new dataframe from the rest of the rows. The names in this case are taken from the first "good" row and the types from the first data row
This is certainly not fast, it's a last resort solution. If I had a better solution I'd use it:
data = df
if(first_col not in df.columns):
# Skip rows until we find the first col header
for i, row in df.iterrows():
if row[0] == first_col:
data = df.iloc[(i + 1):].reset_index(drop=True)
# Read the column names
series = df.iloc[i]
series = series.str.strip()
data.columns = list(series)
# Use only existing column types
types = {k: v for k, v in dtype.items() if k in data.columns}
# Apply the column types again
data = data.astype(dtype=types)
break
return data
In this case the condition is finding the first column name (first_col) in the first cell.
This can be adopted to use different conditions, eg looking for the first numeric cell:
columns = ["x", "y"]
dtypes = {"x":"float64", "y": "float64"}
data = df
# Skip until we find the first numeric value
for i, row in df.iterrows():
if row[0].isnumeric():
data = df.iloc[(i + 1):].reset_index(drop=True)
# Apply names and types
data.columns = columns
data = data.astype(dtype=dtypes)
break
return data
i have got a csv file that is setup with information on top of information and im struggling to read it into a dataframe.
The raw CSV Looks like:
I am hoping to get essentially 3 different things:
1) Define the date and company name in the first row
2) Put the summary table (top table) into a dataframe
3) Put the detailed sales table into another dataframe
I tried df = pd.read_cs(filepath,error_bad_lines=False) which just gives me the summary table but in only 3 rows due to the first row being only 3 columns. Any ideas on how to read these files? the row numbers for the summary table are not fixed (varies how many rows). Any help would be much appreciated! Thanks!
You can specify the number of rows you want to read with parameter nrows and you can also use skiprows to skip reading certain rows in pd.read_excel:
You can read the top-table like below into a df:
Here, you can skip the first row which has some not useful headers and read the next 10 rows which contain top-table's data.
df1 = pd.read_excel('test.xls', skiprows = 1, nrows= 10, usecols = 'A:D')
Then the second-table in another df like this:
Here, you can skip the rows already read in df1, and read the remaining data from the file.
df2 = pd.read_excel('test.xls', skiprows = 6)
For those interested this is what i used to solve the problem:
from csv import reader
with open('*.csv', 'r') as read_obj:
csv_reader = reader(read_obj)
list1 = []
list2 = []
list3 = []
for row in csv_reader:
if len(row) == 3:
list1.append(row)
if len(row) == 4:
list2.append(row)
if len(row) == 7:
list3.append(row)
df1 = pd.DataFrame(list1)
df2 = pd.DataFrame(list2)
df3 = pd.DataFrame(list3)
I have a csv file, where the columns are separated by tab delimiter but the number of columns is not constant. I need to read the file up to the 5th column. (I dont want to ready the whole file and then extract the columns, I would like to read for example line by line and skip the remaining columns)
You can use usecols argument in pd.read_csv to limit the number of columns to be read.
# test data
s = '''a,b,c
1,2,3'''
with open('a.txt', 'w') as f:
print(s, file=f)
df1 = pd.read_csv("a.txt", usecols=range(1))
df2 = pd.read_csv("a.txt", usecols=range(2))
print(df1)
print()
print(df2)
# output
# a
#0 1
#
# a b
#0 1 2
You can use pandas nrows to read only a certain number of csv lines:
import pandas as pd
df = pd.read_csv('out122.txt', usecols=[0,1,2,3,4])
I have a .csv to read into a DataFrame and the names of the columns are in the same .csv file in the previos rows. Usually I drop all the 'unnecesary' rows to create the DataFrame and then hardcode the names of each dataframe
Trigger time,2017-07-31,10:45:38
CH,Signal name,Input,Range,Filter,Span
CH1, "Tin_MIX_Air",TEMP,PT,Off,2000.000000,-200.000000,degC
CH2, "Tout_Fan2b",TEMP,PT,Off,2000.000000,-200.000000,degC
CH3, "Tout_Fan2a",TEMP,PT,Off,2000.000000,-200.000000,degC
CH4, "Tout_Fan1a",TEMP,PT,Off,2000.000000,-200.000000,degC
Here you can see the rows where the columns names are in double quotes "TinMix","Tout..",etc there are exactly 16 rows with names
Logic/Pulse,Off
Data
Number,Date&Time,ms,CH1,CH2,CH3,CH4,CH5,CH7,CH8,CH9,CH10,CH11,CH12,CH13,CH14,CH15,CH16,CH20,Alarm1-10,Alarm11-20,AlarmOut
NO.,Time,ms,degC,degC,degC,degC,degC,degC,%RH,%RH,degC,degC,degC,degC,degC,Pa,Pa,A,A1234567890,A1234567890,A1234
1,2017-07-31 10:45:38,000,+25.6,+26.2,+26.1,+26.0,+26.3,+25.7,+43.70,+37.22,+25.6,+25.3,+25.1,+25.3,+25.3,+0.25,+0.15,+0.00,LLLLLLLLLL,LLLLLLLLLL,LLLL
And here the values of each variables start.
What I need to do is create a Dataframe from this .csv and place these names in the columns names. I'm new to Python and I'm not very sure how to do it
import pandas as pd
path = r'path-to-file.csv'
data=pd.DataFrame()
with open(path, 'r') as f:
for line in f:
data = pd.concat([data, pd.DataFrame([tuple(line.strip().split(','))])], ignore_index=True)
data.drop(data.index[range(0,29)],inplace=True)
x=len(data.iloc[0])
data.drop(data.columns[[0,1,2,x-1,x-2,x-3]],axis=1,inplace=True)
data.reset_index(drop=True,inplace=True)
data = data.T.reset_index(drop=True).T
data = data.apply(pd.to_numeric)
This is what I've done so far to get my dataframe with the usefull data, I'm dropping all the other columns that arent useful to me and keeping only the values. Last three lines are to reset row/column indexes and to transform the whole df to floats. What I would like is to name the columns with each of the names I showed in the first piece of coding as a I said before I'm doing this manually as:
data.columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p']
But I would like to get them from the .csv file since theres a possibility on changing the CH# - "Name" combination
Thank you very much for the help!
Comment: possible for it to work within the other "OPEN " loop that I have?
Assume Column Names from Row 2 up to 6, Data from Row 7 up to EOF.
For instance (untested code)
data = None
columns = []
with open (path) as fh:
for row, line in enumerate (fh, 1):
if row > 2 and row <= 6:
ch, name = line.split(',')[:2]
columns.append(name)
else:
row_data = [tuple(line.strip().split(','))]
if not data:
data = pd.DataFrame(row_data, columns=columns, ignore_index=True)
else:
data.append(row_data)
Question: ... I would like to get them from the .csv file
Start with:
with open (path) as fh:
for row, line in enumerate (fh, 1):
if row > 2:
ch, name = line.split(',')[:2]