I am trying to read an Excel file using pandas but my columns and index are changed:
df = pd.read_excel('Assignment.xlsx',sheet_name='Assignment',index_col=0)
Excel file:
Jupyter notebook:
By default pandas consider first row as header. You need to tell that take 2 rows as header.
df = pd.read_excel("xyz.xlsx", header=[0,1], usecols = "A:I", skiprows=[0])
print df
You can choose to mention skiprows depending on the requirement. If you remove skiprows, it will show first row header without any unnamed entries.
Refer this link
Related
I'm trying to format an Amazon Vendor CSV using Pandas but I'm running into an issue. The issue stems from the fact that Amazon inserts a row with report information before the headers.
When trying to skip over that row when assigning headers to the dataframe, not all columns are captured. Below is my attempt at explicitly stating which row to pull columns from but it doesn't appear to be correct.
df = pd.read_csv(path + 'Amazon Search Terms_Search Terms_US.csv', sep=',', error_bad_lines=False, index_col=False, encoding='utf-8')
headers = df.loc[0]
new_df = pd.DataFrame(df.values[1:], columns=headers)
print('Copying data into new data frame....')
Before it looks like this(I want row 2 to be all the columns in the new df:
After the fact it looks like this(it only selects 5):
I've also tried having it skiprows when opening the CSV, it doesn't treat the report row as data so it just ends up skipping actual data. Not really sure what is going wrong here, any help would be appreciated.
As posted in the comment by #suvayu, adding header=1 into the read csv did the job.
We have a data system that creates tables of data as Excel files. I'm trying to import this Excel file into a pandas dataframe.
In the Excel, Row 1 is some metadata I don't want, while row 2 is the column header. By default, Pandas correctly uses column 1 as the index (a lot number), but the second column is a production date, but it for what ever reason does not have a header in row 2.
So pandas seems to be creating a multi-index by default, is there a way to suppress this function? It seems to be doing this because there is no column header in row 2 column 2 (cell B2). If I manually edit the Excel to add a label, it imports as I want.
import pandas as pd
xlsx01 = pd.ExcelFile("C:/Users/maherp/Desktop/JunkFiles/Book1.xlsx")
df_01 = pd.read_excel(xlsx01, header=1)
I get an error that I cannot decipher when I try:
df_01 = pd.read_excel(xlsx01, header=1, index_col=0)
As suggested by #Peej1226, here is final solution which worked.
df_01 = pd.read_excel(xlsx01, sheet_name='Discrete', skiprows=1, header=0,index_col=0)
I am trying to save a csv to a folder after making some edits to the file.
Every time I use pd.to_csv('C:/Path of file.csv') the csv file has a separate column of indexes. I want to avoid printing the index to csv.
I tried:
pd.read_csv('C:/Path to file to edit.csv', index_col = False)
And to save the file...
pd.to_csv('C:/Path to save edited file.csv', index_col = False)
However, I still got the unwanted index column. How can I avoid this when I save my files?
Use index=False.
df.to_csv('your.csv', index=False)
There are two ways to handle the situation where we do not want the index to be stored in csv file.
As others have stated you can use index=False while saving your
dataframe to csv file.
df.to_csv('file_name.csv',index=False)
Or you can save your dataframe as it is with an index, and while reading you just drop the column unnamed 0 containing your previous index.Simple!
df.to_csv(' file_name.csv ')
df_new = pd.read_csv('file_name.csv').drop(['unnamed 0'],axis=1)
If you want no index, read file using:
import pandas as pd
df = pd.read_csv('file.csv', index_col=0)
save it using
df.to_csv('file.csv', index=False)
As others have stated, if you don't want to save the index column in the first place, you can use df.to_csv('processed.csv', index=False)
However, since the data you will usually use, have some sort of index themselves, let's say a 'timestamp' column, I would keep the index and load the data using it.
So, to save the indexed data, first set their index and then save the DataFrame:
df.set_index('timestamp')
df.to_csv('processed.csv')
Afterwards, you can either read the data with the index:
pd.read_csv('processed.csv', index_col='timestamp')
or read the data, and then set the index:
pd.read_csv('filename.csv')
pd.set_index('column_name')
Another solution if you want to keep this column as index.
pd.read_csv('filename.csv', index_col='Unnamed: 0')
If you want a good format next statement is the best:
dataframe_prediction.to_csv('filename.csv', sep=',', encoding='utf-8', index=False)
In this case you have got a csv file with ',' as separate between columns and utf-8 format.
In addition, numerical index won't appear.
Here the the code to process and save csv file, and raw input csv file and output csv file, using pandas on Python 2.7 and wondering why there is an additional column at the beginning when saving the file? Thanks.
c_a,c_b,c_c,c_d
hello,python,pandas,0.0
hi,java,pandas,1.0
ho,c++,numpy,0.0
sample = pd.read_csv('123.csv', header=None, skiprows=1,
dtype={0:str, 1:str, 2:str, 3:float})
sample.columns = pd.Index(data=['c_a', 'c_b', 'c_c', 'c_d'])
sample['c_d'] = sample['c_d'].astype('int64')
sample.to_csv('saved.csv')
Here is the saved file, there is an additional column at the beginning, whose values are 0, 1, 2.
cat saved.csv
,c_a,c_b,c_c,c_d
0,hello,python,pandas,0
1,hi,java,pandas,1
2,ho,c++,numpy,0
The additional column corresponds to the index of the dataframe and is aggregated once you read the CSV file. You can use this index to slice, select or sort your DF in an effective manner.
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.html
http://pandas.pydata.org/pandas-docs/stable/indexing.html
If you want to avoid this index, you can set the index flag to False when you save your dataframe with the function pd.to_csv. Also, you are removing the header and aggregating it later, but you can use the header of the CSV to avoid this step.
sample = pd.read_csv('123.csv', dtype={0:str, 1:str, 2:str, 3:float})
sample.to_csv('output.csv', index= False)
Hope it helps :)
I read an Excel Sheet into a pandas DataFrame this way:
import pandas as pd
xl = pd.ExcelFile("Path + filename")
df = xl.parse("Sheet1")
the first cell's value of each column is selected as the column name for the dataFrame, I want to specify my own column names, How do I do this?
This thread is 5 years old and outdated now, but still shows up on the top of the list from a generic search. So I am adding this note. Pandas now (v0.22) has a keyword to specify column names at parsing Excel files. Use:
import pandas as pd
xl = pd.ExcelFile("Path + filename")
df = xl.parse("Sheet 1", header=None, names=['A', 'B', 'C'])
If header=None is not set, pd seems to consider the first row as header and delete it during parsing. If there is indeed a header, but you dont want to use it, you have two choices, either (1) use "names" kwarg only; or (2) use "names" with header=None and skiprows=1. I personally prefer the second option, since it clearly makes note that the input file is not in the format I want, and that I am doing something to go around it.
I think setting them afterwards is the only way in this case, so if you have for example four columns in your DataFrame:
df.columns = ['W','X','Y','Z']
If you know in advance what the headers in the Excelfile are its probably better to rename them, this would rename W into A, etc:
df.rename(columns={'W':'A', 'X':'B', etc})
As Ram said, this post comes on the top and may be useful to some....
In pandas 0.24.2 (may be earlier as well), read_excel itself has the capability of ignoring the source headers and giving your own col names and few other good controls:
DID = pd.read_excel(file1, sheet_name=0, header=None, usecols=[0, 1, 6], names=['A', 'ID', 'B'], dtype={2:str}, skiprows=10)
# for example....
# usecols => read only specific col indexes
# dtype => specifying the data types
# skiprows => skip number of rows from the top.
call .parse with header=None keyword argument.
df = xl.parse("Sheet1", header=None)
In case the excel sheet only contains the data without headers:
df=pd.read_excel("the excel file",header=None,names=["A","B","C"])
In case the excel sheet already contains header names, then use skiprows to skip the line:
df=pd.read_excel("the excel file",header=None,names=["A","B","C"],skiprows=1)