def loadCsv(filename):
lines = csv.reader(open('diabetes.csv'))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]
return dataset
Hello, I'm trying to implement Naive-Bayes but its giving me this error even though i've manually changed the type of each column to float.
it's still giving me error.
Above is the function to convert.
The ValueError is because the code is trying to cast (convert) the items in the CSV header row, which are strings, to floats. You could just skip the first row of the CSV file, for example:
for i in range(1, len(dataset)): # specifying 1 here will skip the first row
dataset[i] = [float(x) for x in dataset[i]
Note: that would leave the first item in dataset as the headers (str).
Personally, I'd use pandas, which has a read_csv() method, which will load the data directly into a dataframe.
For example:
import pandas as pd
dataset = pd.read_csv('diabetes.csv')
This will give you a dataframe though, not a list of lists. If you really want a list of lists, you could use dataset.values.tolist().
Related
I have a Pandas DataFrame, one column, is an OrderedSet like this:
df
OrderedSetCol
0 OrderedSet([1721754, 3622558, 2550234, 2344034, 8550040])
This is:
from ordered_set import OrderedSet
I am just trying to convert this column into list:
df['OrderedSetCol_list'] = df['OrderedSetCol'].apply(lambda x: ast.literal_eval(str("\'" + x.replace('OrderedSet(','').replace(')','') + "\'")))
The code executes succesfully, but, my column type is still str and not list
type(df.loc[0]['OrderedSetCol_list'])
str
What am I doing wrong?
EDIT: My OrderedSetCol is also a string column as I am reading a file from a disk, which was originally saved from OrderedSet column.
Expected Output:
[1721754, 3622558, 2550234, 2344034, 8550040]
You can apply a list calling just like you would do with the OrderedSet itself:
df = pd.DataFrame({'OrderedSetCol':[OrderedSet([1721754, 3622558, 2550234, 2344034, 8550040])]})
df.OrderedSetCol.apply(list)
Output:
[1721754, 3622558, 2550234, 2344034, 8550040]
If your data type string column:
df.OrderedSetCol.str.findall('\d+')
I am new to python3 and trying to do chisquared tests on columns in a pandas dataframe. My columns are in pairs: observed_count_column_1, expected count_column_1, observed_count_column_2, expected_count_column_2 and so on. I would like to make a loop to get all column pairs done at once.
I succeed doing this if I specify column index integers or column names manually.
This works
from scipy.stats import chisquare
import pandas as pd
df = pd.read_csv (r'count.csv')
chisquare(df.iloc[:,[0]], df.iloc[:,[1]])
This, trying with a loop, does not:
from scipy.stats import chisquare
import pandas as pd
df = pd.read_csv (r'count.csv')
for n in [0,2,4,6,8,10]:
chisquare(df.iloc[:,[n]], df.iloc[:,[n+1]]
The loop code does not seem to run at all and I get no error but no output either.
I was wondering why this is happening and how can I actually approach this?
Thank you,
Dan
Consider building a data frame of chi-square results from list of tuples, then assign column names as indicators for observed and expected frequencies (subsetting even/odd columns by indexed notation):
# CREATE DATA FRAME FROM LIST IF TUPLES
# THEN ASSIGN COLUMN NAMES
chi_square_df = (pd.DataFrame([chisquare(df.iloc[:,[n]], df.iloc[:,[n+1]]) \
for n in range(0,11,2)],
columns = ['chi_sq_stat', 'p_value'])
.assign(obs_freq = df.columns[::2],
exp_freq = df.columns[1::2])
)
chisquare() function returns two values so you can try this:
for n in range(0, 11, 2):
chisq, p = chisquare(df.iloc[:,[n]], df.iloc[:,[n+1]]
print('Chisq: {}, p-value: {}'.format(chisq, p))
You can find what it returns in the docs here https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html
Thank you for the suggestions. Using the information from Parfait comment, that loops don't print I managed to find a solution, although not as elegant as their own solution above.
for n in range(0, 11, 2):
print(chisquare(df.iloc[:,[n]], df.iloc[:,[n+1]]))
This gives the expected results.
Dan
New to Python. I'm importing a CSV, then if any data is missing I need to return a CSV with an additional column to indicate which rows are missing data. Colleague suggested that I import CSV into a dataframe, then create a new dataframe with a "Comments" column, fill it with a comment on the intended rows, and append it to the original dataframe. I'm stuck at the step of filling my new dataframe, "dferr", with the correct number of rows that would match up to "dfinput".
Have Googled, "pandas csv return error column where data is missing", but haven't found anything related to creating a new CSV that marks bad rows. I don't even know if the proposed way is the best way to go about this.
import pandas as pd
dfinput = None
try:
dfinput = pd.read_csv(r"C:\file.csv")
except:
print("Uh oh!")
if dfinput is None:
print("Ack!")
quit(10)
dfinput.reset_index(level=None, drop=False, inplace=True, col_level=0,
col_fill='')
dferr = pd.DataFrame(columns=['comment'])
print("Empty DataFrame", dferr, sep='\n')
Expected results: "dferr" would have an index column with number of rows equal to "dfinput", and comments on the correct rows where "dfinput" has missing values.
Actual results: "dferr" is empty.
My understanding of 'missing data' here would be null values. It seems that for every row, you want the names of null fields.
df = pd.DataFrame([[1,2,3],
[4,None,6],
[None,8,None]],
columns=['foo','bar','baz'])
# Create a dataframe of True/False, True where a criterion is met
# (in this case, a null value)
nulls = df.isnull()
# Iterate through every row of *nulls*,
# and extract the column names where the value is True by boolean indexing
colnames = nulls.columns
null_labels = nulls.apply(lambda s:colnames[s], axis=1)
# Now you have a pd.Series where every entry is an array
# (technically, a pd.Index object)
# Pandas arrays have a vectorized .str.join method:
df['nullcols'] = null_labels.str.join(', ')
The .apply() method in pandas can sometimes be a bottleneck in your code; there are ways to avoid using this, but here it seemed to be the simplest solution I could think of.
EDIT: Here's an alternate one-liner (instead of using .apply) that might cut down computation time slightly:
import numpy as np
df['nullcols'] = [colnames[x] for x in nulls.values]
This might be even faster (a bit more work is required):
np.where(df.isnull(),df.columns,'')
EDIT: This was Excel's fault changing the data type, not Pandas.
When I read a CSV using pd.read_csv(file) a column of super long ints gets converted to a low res float. These ints are a date time in microseconds.
example:
CSV Columns of some values:
15555071095204000
15555071695202000
15555072295218000
15555072895216000
15555073495207000
15555074095206000
15555074695212000
15555075295202000
15555075895210000
15555076495216000
15555077095230000
15555077695206000
15555078295212000
15555078895218000
15555079495209000
15555080095208000
15555080530515000
15555086531880000
15555092531889000
15555098531886000
15555104531886000
15555110531890000
15555116531876000
15555122531873000
15555128531884000
15555134531884000
15555140531887000
15555146531874000
pd.read_csv produces: 1.55551e+16
how do I get it to report the exact int?
I've tried using: float_precision='high'
It's possible that this is caused by the way Pandas handles missing values, meaning that your column is importing as floats, to allow the missing values to be coded as NaN.
A simple solution would be to force the column to import as a str, then impute or remove missing values, and the convert to int:
import pandas as pd
df = pd.read_csv(file, dtypes={'col1': str}) # Edit to use appropriate column reference
# If you want to just remove rows with missing values, something like:
df = df[df.col1 != '']
# Then convert to integer
df.col1 = df.col1.astype('int64')
With a Minimal, Complete and Verifiable Example we can pinpoint the problem and update the code to accurately solve it.
Is there any option in pandas' read_csv function that can automatically convert every item of an object dtype as str.
For example, I get the following when trying to read a CSV file:
mydata = pandas.read_csv(myfile, sep="|", header=None)
C:\...\pandas\io\parsers.py:1159: DtypeWarning: Columns (6,635) have mixed types. Specify dtype option on import or set low_memory=False.
data = self._reader.read(nrows)
Is there a way such that (i) the warning is suppressed from printing, but (ii) I can capture the warning message in a string from where I can extract the specific columns, e.g. 6 and 635 in this case (so that I can fix the dtype subsequently)? Or, alternatively, if I can specify whenever there are mixed types, the read_csv function should convert the values in that column to str ?
I'm using Python 3.4.2 and Pandas 0.15.2
The Dtypewarning is a Warning which can be caught and acted on. See here for more information. To catch the warning we need to wrap the execution in a warnings.catch_warnings block. The warning message and columns affected can be extracted using regex, then used to set the correct column type using .astype(target_type)
import re
import pandas
import warnings
myfile = 'your_input_file_here.txt'
target_type = str # The desired output type
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
mydata = pandas.read_csv(myfile, sep="|", header=None)
print("Warnings raised:", ws)
# We have an error on specific columns, try and load them as string
for w in ws:
s = str(w.message)
print("Warning message:", s)
match = re.search(r"Columns \(([0-9,]+)\) have mixed types\.", s)
if match:
columns = match.group(1).split(',') # Get columns as a list
columns = [int(c) for c in columns]
print("Applying %s dtype to columns:" % target_type, columns)
mydata.iloc[:,columns] = mydata.iloc[:,columns].astype(target_type)
The result should be the same DataFrame with the problematic columns set to a str type. It is worth noting that string columns in a Pandas DataFrame are reported as object.
As noted in the error message itself, the simplest way to avoid pd.read_csv from returning mixed dtypes is to set low_memory=False:
df = pd.read_csv(..., low_memory=False)
This luxury is however not available when concatenating multiple dataframes using pd.concat.