I'm currently trying to use Python read a text file into Sqlite3 using Pandas. Here are a few entries from the text file:
1 Michael 462085 2.2506 Jessica 302962 1.5436
2 Christopher 361250 1.7595 Ashley 301702 1.5372
3 Matthew 351477 1.7119 Emily 237133 1.2082
The data consists of popular baby names, and I have to separate male names and female names into their own tables and perform queries on them. My method consists of first placing all the data into both tables, then dropping the unneeded columns afterwards. My issue is that when I try to add names to the columns, I get a value error: The expected axis has 6 elements, but 7 values. I'm assuming it's because Pandas possibly isn't reading the last values of each line, but I can't figure out how to fix it. My current delimiter is a whitespace delimiter that you can see below.
Here is my code:
import sqlite3
import pandas as pd
import csv
con = sqlite3.connect("C:\\****\\****\\****\\****\\****\baby_names.db")
c=con.cursor()
# Please note that most of these functions will be commented out, because they will only be run once.
def create_and_insert():
# load data
df = pd.read_csv('babynames.txt', index_col=0, header=None, sep= '\s+', engine = 'python')
# Reading the textfile
df.columns = ['Rank', 'BoyName', 'Boynumber', 'Boypercent', 'Girlname', 'Girlnumber', 'Girlpercent']
# Adding Column names
df.columns = df.columns.str.strip()
con = sqlite3.connect("*************\\baby_names.db")
# drop data into database
df.to_sql("Combined", con)
df.to_sql("Boys", con)
df.to_sql("Girls", con)
con.commit()
con.close()
create_and_insert()
def test():
c.execute("SELECT * FROM Boys WHERE Rank = 1")
print(c.fetchall())
test()
con.commit()
con.close()
I've tried adding multiple delimiters, but it didn't seem to do anything. Using just regular space as the delimiter seems to just create 'blank' column names. From reading the Pandas docs, it says that multiple delimiters are possible, but I can't quite figure it out. Any help would be greatly appreciated!
Note that:
your input file contains 7 columns,
but the initial column is set as the index (you passed index_col=0),
so your DataFrame contains only 6 regular columns.
Print df to confirm it.
Now, when you run df.columns = ['Rank', ...], you attempt to assing the
7 passed names to existing 6 data columns.
Probably you should:
read the DataFrame without setting the index (for now),
assign all 7 column names,
set Rank column as the index.
The code to do it is:
df = pd.read_csv('babynames.txt', header=None, sep='\s+', engine='python')
df.columns = ['Rank', 'BoyName', 'Boynumber', 'Boypercent', 'Girlname', 'Girlnumber',
'Girlpercent']
df.set_index('Rank', inplace=True)
Or even shorter (all in one):
df = pd.read_csv('babynames.txt', sep='\s+', engine='python',
names=['Rank', 'BoyName', 'Boynumber', 'Boypercent', 'Girlname', 'Girlnumber',
'Girlpercent'], index_col='Rank')
Related
I have these below columns in my csv . Usually all these columns have value like below and the code works smoothly .
dec list_namme list device Service Gate
12 food cookie 200.56.57.58 Shop 123
Now I encountered issue, I got one csv file that has all these columns but there is no content for them. Here it looks like..
dec list_namme list device Service Gate
and once the code runs over it , it creates new csv with below columns that was not expected. I got new columns name as index and also , instead of 3(device service Gate) columns I am getting wrong 2.
index Gate
For the csv having contents I didnot faced any issue , even the columns are coming correctly.
Below is the code.
The code is :
if os.path.isfile(client_csv_file):
df=pd.read_csv(csv_file) #Read CSV
df['Gate']=df.Gate.astype(str)
df = df.groupby(['device', 'Service'])['Gate'].apply(lambda x: ', '.join(set(x))).reset_index()
df.to_csv(client_out_file, index=False)
Please help me in this code to fix this.
Performing a groupby on an empty dataframe is resulting in a dataframe without groupby-key columns.
One solution is to test if your dataframe is empty before performing manipulations:
if os.path.isfile(client_csv_file):
df = pd.read_csv(csv_file)
if df.empty:
df = df[['device', 'Service', 'Gate']]
else:
df['Gate'] = df.Gate.astype(str)
df = df.groupby(['device', 'Service'])['Gate']\
.apply(lambda x: ', '.join(set(x))).reset_index()
df.to_csv(client_out_file, index=False)
I'm splitting a large CSV (containing stock financial data) file into smaller chunks. The format of the CSV file is different. Something like an Excel pivot table. The first few rows of the first column contain some headers.
Company name, id, etc. are repeated across the following columns. Because one single company has more than one attribute, not like one company has one column only.
After the first few rows, the columns then start resembling a typical data frame where headers are in columns instead of rows.
Anyways, what I'm trying to do is to make Pandas allow duplicate column headers and not make it add ".1", ".2", ".3", etc after the headers. I know Pandas does not allow this natively, is there a workaround? I tried to set header = None on read_csv but it throws a tokenization error which I think makes sense. I just can't think of an easy way.
import pandas as pd
csv_path = "C:\\Users\\ThirdHandBD\\Desktop\\Data Splitting\\pd-split\\chunk4.csv"
#df = pd.read_csv(csv_path, header=1, dtype='unicode', sep=';', low_memory=False, error_bad_lines=False)
df = pd.read_csv(csv_path, header = 1, dtype='unicode', sep=';', index_col=False)
print("I read in a dataframe with {} columns and {} rows.".format(
len(df.columns), len(df)
))
filename = 1
#column increment
x = 30 * 59
for column in df:
loc = df.columns.get_loc(column)
if loc == (x * filename) + 1:
y = filename - 1
a = (x * y) + 1
b = (x * filename) + 1
date_df = df.iloc[:, :1]
out_df = df.iloc[:, a:b]
final_df = pd.concat([date_df, out_df], axis=1, join='inner')
out_path = "C:\\Users\\ThirdHandBD\\Desktop\\Data Splitting\\pd-split\\chunk4-part" + str(filename) + ".csv"
final_df.to_csv(out_path, index=False)
#out_df.to_csv(out_path)
filename += 1
# This should be the same as df, but with only the first column.
# Check it with similar code to above.
EDIT:
From, https://github.com/pandas-dev/pandas/issues/19383, I add:
final_df.columns = final_df.iloc[0]
final_df = final_df.reindex(final_df.index.drop(0)).reset_index(drop=True)
So, full code:
import pandas as pd
csv_path = "C:\\Users\\ThirdHandBD\\Desktop\\Data Splitting\\pd-split\\chunk4.csv"
#df = pd.read_csv(csv_path, header=1, dtype='unicode', sep=';', low_memory=False, error_bad_lines=False)
df = pd.read_csv(csv_path, header = 1, dtype='unicode', sep=';', index_col=False)
print("I read in a dataframe with {} columns and {} rows.".format(
len(df.columns), len(df)
))
filename = 1
#column increment
x = 30 * 59
for column in df:
loc = df.columns.get_loc(column)
if loc == (x * filename) + 1:
y = filename - 1
a = (x * y) + 1
b = (x * filename) + 1
date_df = df.iloc[:, :1]
out_df = df.iloc[:, a:b]
final_df = pd.concat([date_df, out_df], axis=1, join='inner')
out_path = "C:\\Users\\ThirdHandBD\\Desktop\\Data Splitting\\pd-split\\chunk4-part" + str(filename) + ".csv"
final_df.columns = final_df.iloc[0]
final_df = final_df.reindex(final_df.index.drop(0)).reset_index(drop=True)
final_df.to_csv(out_path, index=False)
#out_df.to_csv(out_path)
filename += 1
# This should be the same as df, but with only the first column.
# Check it with similar code to above.
Now, the entire first row is gone. But, the expected output is for the header row to be replaced with the reset index, without the ".1", ".2", etc.
Screenshot:
The SimFin ID row is no longer there.
This is how I did it:
final_df.columns = final_df.columns.str.split('.').str[0]
Reference:
https://pandas.pydata.org/pandas-docs/stable/text.html
Below solution would ensure that other column names with symbol period ('.') in the dataframe do not get modified
import pandas as pd
from csv import DictReader
csv_file_loc = "file.csv"
# Read csv
df = pd.read_csv(csv_file_loc)
# Get column names from csv file using DictReader
col_names = DictReader(open(csv_file_loc, 'r')).fieldnames
# Rename columns
df.columns = col_names
I know I'm pretty late to the draw on this one, but I'm leaving the solution I came up with in case anyone else wanders across this as I have.
Firstly, the linked question has a pretty nice and dynamic solution that seems to work well even for high column counts. I came across that after I made my solution, haha. Check it out here. Another answer on this thread utilizes the csv library to read and use the column names from that, as it doesn't seem to modify duplicates like Pandas does. That should work fine, but I just wanted to avoid using any extra libraries, especially considering I was originally using csv and then upgrade to Pandas for better functionality.
Now here's my solution. I'm sure it could be done more nicely but this does the job for what I needed and is pretty dynamic, from what I can tell. It basically goes through the columns, checks if it can split the string based on the rightmost "." (that's the rpartition), then does a few more checks from there.
It checks:
Is this string in the colMap? The colMap keeps track of all of the column names, duplicate or not. If this comes back true, then that means it's a duplicate of another column that came before it.
Is the string after the rightmost "." a number? All of the columns are strings, so this just makes sure that whatever it is can be converted into a number to prevent grabbing some other random column that meets previous criteria but isn't actually a dupe from Pandas. eg. "DupeCol" and "DupeCol.Stuff" wouldn't get picked up, but "DupeCol" and "DupeCol.1" would.
Does the number that comes after the rightmost "." match up to the current count of duplicates in the colMap? Seeing as the colMap contains all of the names of the columns, duplicates or not, this will ensure that we're not grabbing a user-named column that managed to overlap with the ".number" convention that Pandas uses. Eg. if a user had named two columns "DupeCol" and "DupeCol.6", it wouldn't get picked up unless there were 6 "DupeCol"s preceding "DupeCol.6", indicating that it almost had to be Pandas that named it that way, as opposed to the user. This part is definitely a bit overkill, but I felt like being extra thorough.
colMap = []
for col in df.columns:
if col.rpartition('.')[0]:
colName = col.rpartition('.')[0]
inMap = col.rpartition('.')[0] in colMap
lastIsNum = col.rpartition('.')[-1].isdigit()
dupeCount = colMap.count(colName)
if inMap and lastIsNum and (int(col.rpartition('.')[-1]) == dupeCount):
colMap.append(colName)
continue
colMap.append(col)
df.columns = colMap
Hopefully this helps someone! Feel free to comment if you think it could use any improvements. I don't entirely love using "continue" in my code, but I'm not sure if that's because it's actually bad practice or just me reading random people complain about it too much. I think it doesn't make the code too unreadable here and prevents the need for duplicating the "else" statement; but let me know if there's a way to improve that or anything otherwise. I'm always looking to learn!
If you know types of all data you may consider loading the csv without header first.
df = pd.read_csv(csv_file, header=None)
df.columns = df.iloc[0] # replace column with first row
df = df.drop(0) # remove the first row
(Note that drop is to remove the row, given that your index is unique, and may not be true if you use index_col argument of pd.read_csv)
caveats: The above solution causes you to lose dtypes infomations.
There is some solution to fix the above problem.
# turn each column into numeric
df = df.apply(lambda col: pd.to_numeric(col, errors='ignore'), axis=0)
Otherwise, you may consider reading the csv twice to get the dtype information and apply the correct convertion.
Hey guys I am have an data that looks like this train.dat . I am trying to create an varible that will contain the [ith] value of the column containing(-1,or 1), and another variable to hold the value of column that have strings.
So far I have tried this,
df=pd.read_csv("train.dat",delimiter="\t", sep=',')
# print(df.head())
# separate names from classes
vals = df.ix[:,:].values
names = [n[0][3:] for n in vals]
cls = [n[0][0:] for n in vals]
print(cls)
However the output looks all jumbled up, any help would be appreciated. I am a begineer in python
If the character after the numerical value is a tab, you're fine and all you would need is
import io # using io.StringIO for demonstration
import pandas as pd
ratings = "-1\tThis movie really sucks.\n-1\tRun colored water through
a reflux condenser and call it a science movie?\n+1\tJust another zombie flick? You'll be surprised!"
df = pd.read_csv(io.StringIO(ratings), sep='\t',
header=None, names=['change', 'rating'])
Passing header=None makes sure that the first line is interpreted as data.
Passing names=['change', 'rating'] provides some (reasonable) column headers.
Of course, the character is not a tab :D.
import io # using io.string
import pandas as pd
ratings = "-1 This movie really sucks.\n-1 Run colored water through a
reflux condenser and call it a science movie?\n+1 Just another zombie
flick? You'll be surprised!"
df = pd.read_csv(io.StringIO(ratings), sep='\t',
header=None, names=['stuff'])
df['change'], df['rating'] = df.stuff.str[:3], df.stuff.str[3:]
df.drop('stuff', axis=1)
One viable option is to read in the whole rating as one temporary column, split the string, distribute it to two columns and eventually drop the temporary column.
I am importing a web log text file in Python using Pandas. Python is reading the headers however has used the text "Fields:" as a header and has then added another column of blanks (NaN's) at the end. How can I stop this text being used as a column heading?
here is my code:
arr = pd.read_table("path", skiprows=3, delim_whitespace=True, na_values=True)
Here is the start of the file:
Software: Microsoft Internet Information Services 7.5
Version: 1.0
Date: 2014-08-01 00:00:25
Fields: date time
2014-08-01 00:00:25...
Result is that 'Fields' is being used as a column heading and a column full of NaN values is being created for column 'time'.
You can do it calling read_table twice.
# reads the forth line into 1x1 df being a string,
# then splits it and skips the first field:
col_names = pd.read_table('path', skiprows=3, nrows=1, header=None).iloc[0,0].split()[1:]
# reads the actual data:
df = pd.read_table('path', sep=' ', skiprows=4, names=col_names)
If you already know the names of the columns (eg. date and time) then it's even simpler:
df = pd.read_table('path', sep=' ', skiprows=4, names = ['date', 'time'])
I think you may want skiprows = 4 and header = None
I have a .csv that contains contains column headers and is displayed below. I need to suppress the column labeling when I ingest the file as a data frame.
date,color,id,zip,weight,height,locale
11/25/2013,Blue,122468,1417464,3546600,254,7
When I issue the following command:
df = pd.read_csv('c:/temp1/test_csv.csv', usecols=[4,5], names = ["zip","weight"], header = 0, nrows=10)
I get:
zip weight
0 1417464 3546600
I have tried various manipulations of header=True and header=0. If I don't use header=0, then the columns will all print out on top of the rows like so:
zip weight
height locale
0 1417464 3546600
I have tried skiprows= 0 and 1 but neither removes the headers. However, the command works by skipping the line specified.
I could really use some additional insight or a solve. Thanks in advance for any assistance you could provide.
Tiberius
Using the example of #jezrael, if you want to skip the header and suppress de column labeling:
import pandas as pd
import numpy as np
import io
temp=u"""date,color,id,zip,weight,height,locale
11/25/2013,Blue,122468,1417464,3546600,254,7"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), usecols=[4,5], header=None, skiprows=1)
print df
4 5
0 3546600 254
I'm not sure I entirely understand why you want to remove the headers, but you could comment out the header line as follows as long as you don't have any other rows that begin with 'd':
>>> df = pd.read_csv('test.csv', usecols=[3,4], header=None, comment='d') # comments out lines beginning with 'date,color' . . .
>>> df
3 4
0 1417464 3546600
It would be better to comment out the line in the csv file with the crosshatch character (#) and then use the same approach (again, as long as you have not commented out any other lines with a crosshatch):
>>> df = pd.read_csv('test.csv', usecols=[3,4], header=None, comment='#') # comments out lines with #
>>> df
3 4
0 1417464 3546600
I think you are right.
So you can change column names to a and b:
import pandas as pd
import numpy as np
import io
temp=u"""date,color,id,zip,weight,height,locale
11/25/2013,Blue,122468,1417464,3546600,254,7"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), usecols=[4,5], names = ["a","b"], header = 0 , nrows=10)
print df
a b
0 3546600 254
Now these columns have new names instead of weight and height.
df = pd.read_csv(io.StringIO(temp), usecols=[4,5], header = 0 , nrows=10)
print df
weight height
0 3546600 254
You can check docs read_csv (bold by me):
header : int, list of ints, default ‘infer’
Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no names passed, otherwise None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True, so header=0 denotes the first line of data rather than the first line of the file.