I'm trying to use pandas read_excel to work with a file. The file has two columns of headers so I'm trying to use the multiIndex feature apart of the header keyword argument.
import pandas as pd, os
"""data in 2015 MOR Folder"""
filename = 'MOR-JANUARY 2015.xlsx'
print(os.path.isfile(filename))
df1 = pd.read_excel(filename, header=[0,1], sheetname='MOR')
print(df1)
the error I get is ValueError: Length of new names must be 1, got 2. The file is in this google drive folder https://drive.google.com/drive/folders/0B0ynKIVAlSgidFFySWJoeFByMDQ?usp=sharing
I'm trying to follow the solution posted here
Read excel sheet with multiple header using Pandas
I could be mistaken but I don't think pandas handles parsing excel rows where there are merged cells. So in that first row, the merged cells get parsed as mostly empty cells. You'd need them nicely repeated to act correctly. This is what motivates the ffill below. If you could control the Excel workbook ahead of time and you might be able to use the code you have.
my solution
It's not pretty, but it'll get it done.
filename = 'MOR-JANUARY 2015.xlsx'
df1 = pd.read_excel(filename, sheetname='MOR', header=None)
vals = df1.values
mux = pd.MultiIndex.from_arrays(df1.ffill(1).values[:2, 1:], names=[None, 'DATE'])
df1 = pd.DataFrame(df1.values[2:, 1:], df1.values[2:, 0], mux)
Related
I need to add a column to 40 excel files. The new column in each file will be filled with a name.
This is what I have:
files=[16686_Survey.xlsx, 16687_Survey.xlsx, 16772_Survey.xlsx, ...] (40 files with more than 200 rows each)
filenames=['name1', 'name2', 'name3', ...] (40 names)
I need to add a column to each excel file and write its corresponding name along the new column.
With the following code I got what I need for one file.
import pandas as pd
df = pd.read_excel('16686_Survey.xlsx')
df.insert(0, "WellName", "Name1")
writer = pd.ExcelWriter('16686_Survey.xlsx')
df.to_excel(writer, index = False)
writer.save()
But it would be inefficient if I do it 40 times, and I would like to learn how to use a loop to address this type of problem because I have been in the same situation many times.
The image is what I got with the code above. The first table in the image is what I have. The second table is what I want
Thank you for your help!
I'm not a 100% sure I understand your question but I think you're looking for this:
import pandas as pd
files=['16686_Survey.xlsx', '16687_Survey.xlsx', '16772_Survey.xlsx', ...]
filenames=['name1', 'name2', 'name3', ...]
for excel_file, other_name in zip(files, filenames):
df = pd.read_excel(excel_file)
df.insert(0, "WellName", other_name)
writer = pd.ExcelWriter(excel_file)
df.to_excel(writer, index = False)
writer.save()
I combined both the lists (I assumed they were the same length) using the zip function. The zip function takes items from the lists one by one and combines them so that all the first items are together and all the second and so forth.
I'm working on a mechanical engineering project. For the following code, the user enters the number of cylinders that their compressor has. A dataframe is then created with the correct number of columns and is exported to Excel as a CSV file.
The outputted dataframe looks exactly like I want it to as shown in the first link, but when opened in Excel it looks like the image in the second link:
1.my dataframe
2.Excel Table
Why is my dataframe not exporting properly to Excel and what can I do to get the same dataframe in Excel?
import pandas as pd
CylinderNo=int(input('Enter CylinderNo: '))
new_number=CylinderNo*3
list1=[]
for i in range(1,CylinderNo+1):
for j in range(0,3):
Cylinder_name=str('CylinderNo ')+str(i)
list1.append(Cylinder_name)
df = pd.DataFrame(list1,columns =['Kurbel/Zylinder'])
list2=['Triebwerk', 'Packung','Ventile']*CylinderNo
Bauteil = {'Bauteil': list2}
df2 = pd.DataFrame (Bauteil, columns = ['Bauteil'])
new=pd.concat([df, df2], axis=1)
list3=['Nan','Nan','Nan']*CylinderNo
Bewertung={'Bewertung': list3}
df3 = pd.DataFrame (Bewertung, columns = ['Bewertung'])
new2=pd.concat([new, df3], axis=1)
Empfehlung={'Empfehlung': list3}
df4 = pd.DataFrame (Empfehlung, columns = ['Empfehlung'])
new3=pd.concat([new2, df4], axis=1)
new3.set_index('Kurbel/Zylinder')
new3 = new3.set_index('Kurbel/Zylinder', append=True).swaplevel(0,1)
#export dataframe to csv
new3.to_csv('new3.csv')
To be clear, a comma-separated values (CSV) file is not an Excel format type or table. It is a delimited text file that Excel like other applications can open.
What you are comparing is simply presentation. Both data frames are exactly the same. For multindex data frames, Pandas print output does not repeat index values for readability on the console or IDE like Jupyter. But such values are not removed from underlying data frame only its presentation. If you re-order indexes, you will see this presentation changes. The full complete data frame is what is exported to CSV. And ideally for data integrity, you want the full data set exported with to_csv to be import-able back into Pandas with read_csv (which can set indexes) or other languages and applications.
Essentially, CSV is an industry format to store and transfer data. Consider using Excel spreadsheets, HTML markdown, or other reporting formats for your presentation needs. Therefore, to_csv may not be the best method. You can try to build text file manually with Python i/o write methods, with open('new.csv', 'w') as f, but will be an extensive workaround See also #Jeff's answer here but do note the latter part of solution does remove data.
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,'')
guys, I need a bit help on Pandas and would appreciate greatly your inputs.
My original file looks like this:
I would like to convert it by mergering some pairs of columns (generating their averages) and returns a new file looking like this:
Also, if possible, I would also like to split the column 'RateDateTime' into two columns, one contains the date, the other contains only the time. How should I do it? I tried coding as belows but it doesn't work:
import pandas as pd
dateparse = lambda x: pd.datetime.strptime(x, '%Y/%m/%d %H:%M:%S')
df = pd.read_csv('data.csv', parse_dates=['RateDateTime'], index_col='RateDateTime',date_parser=dateparse)
a=pd.to_numeric(df['RateAsk_open'])
b=pd.to_numeric(df['RateAsk_high'])
c=pd.to_numeric(df['RateAsk_low'])
d=pd.to_numeric(df['RateAsk_close'])
e=pd.to_numeric(df['RateBid_open'])
f=pd.to_numeric(df['RateBid_high'])
g=pd.to_numeric(df['RateBid_low'])
h=pd.to_numeric(df['RateBid_close'])
df['Open'] = (a+e) /2
df['High'] = (b+f) /2
df['Low'] = (c+g) /2
df['Close'] = (d+h) /2
grouped = df.groupby('CurrencyPair')
Open=grouped['Open']
High=grouped['High']
Low=grouped['Low']
Close=grouped['Close']
w=pd.concat([Open, High,Low,Close], axis=1, keys=['Open', 'High','Low','Close'])
w.to_csv('w.csv')
Python returns:
TypeError: cannot concatenate object of type "<class 'pandas.core.groupby.groupby.SeriesGroupBy'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid
Can someone help me please? Many thanks!!!
IIUYC, you don't need grouping here. You can simply update existing dataframe with new columns and specify, what columns you need to save to csv file in to_csv method. Here is example:
df['Open'] = df[['RateAsk_open', 'RateBid_open']].mean(axis=1)
df['RateDate'] = df['RateDateTime'].dt.date
df['RateTime'] = df['RateDateTime'].dt.time
df.to_csv('w.csv', columns=['CurrencyPair', 'Open', 'RateDate', 'RateTime'])
I have a reporting sheet in excel that contains a set of datapoints that I want to compile from multiple files with the same format into a master dataset.
The initial step I undertook was to extract the data points I need from multiple sheet into one pandas dataframe. See the steps below
I initally imported the excel file and parsed it
import pandas as pd
xl = pd.ExcelFile(r"C:\Users\Nicola\Desktop\ISP 2016-20 Ops-Technical Form.xlsm")
df = xl.parse("FSL, WASH, DRM") #name of sheet #1
Then I located the data points needed for synthesis
a=df.iloc[5:20,3:5]
a1=df.iloc[6:9,10:12]
b=df.iloc[31:35,3:5]
b1=df.iloc[31:35,10:12]
Then I concatenated and equalised columns positioning to maintain the whole list of values within the same column:
dfcon=pd.concat(([a,b]))
dfcon2=pd.concat(([a1,b1]))
new_cols = {x: y for x, y in zip(dfcon.columns, dfcon2.columns)}
dfcont2=dfcon2.append(dfcon.rename(columns=new_cols))
And lastly created a dataframe with the string of values I need
master=pd.DataFrame(dfcont2)
finalmaster=master.transpose()
The next two steps I wish to pursue are:
1) Replicate the same code for 50 excel files
2) Compile all string of values from this set of excel files into one single pandas dataframe without running this code over again and compile manually by exporting it into excel.
Any support would be greatly appreciated. Thanks
I believe need loop by file names created by glob and last concat together (all files have same structure):
import glob
dfs = []
for f in glob.glob('*.xlsm'):
df = pd.read_excel(io=f, sheet_name=1)
a=df.iloc[5:20,3:5]
a1=df.iloc[6:9,10:12]
b=df.iloc[31:35,3:5]
b1=df.iloc[31:35,10:12]
dfcon=pd.concat(([a,b]))
dfcon2=pd.concat(([a1,b1]))
new_cols = {x: y for x, y in zip(dfcon.columns, dfcon2.columns)}
dfcont2=dfcon2.append(dfcon.rename(columns=new_cols))
dfs.append(dfcont2.T)
out = pd.concat(dfs, ignore_index=True)
Found the solution that works for me, thank you for the input, jezrael.
To further explain:
1) Imported the files with same structure from my Desktop directory, parsed and selected the Excel sheet from which data can be extracted from different locations (iloc)
import glob
dfs = []
for f in glob.glob('C:/Users/Nicola/Desktop/OPS Form/*.xlsm'):
df = pd.ExcelFile(io=f, sheet_name=1)
df = df.parse("FSL, WASH, DRM")
a=df.iloc[5:20,3:5]
a1=df.iloc[7:9,10:12]
b=df.iloc[31:35,3:5]
b1=df.iloc[31:35,10:12]
c=df.iloc[50:56,3:5]
c1=df.iloc[38:39,10:12]
d=df.iloc[57:61,3:5]
e=df.iloc[63:71,3:5]
2) Concatenated and repositioned columns order to compose the first version of the dataframe (output)
dfcon=pd.concat(([a,b,c,d,e]))
dfcon2=pd.concat(([a1,b1,c1]))
new_cols = {x: y for x, y in zip(dfcon.columns, dfcon2.columns)}
dfcont2=dfcon2.append(dfcon.rename(columns=new_cols))
dfs.append(dfcont2.T)
3) Output presented the same string of values but repeated twice [same label and form-specific entry] from recursive data pull-outs linked to iloc locations.
output = pd.concat(dfs, ignore_index=True)
4) This last snippet simply allowed me to extract the label only once and to select all entries ordered in odd numbers. With the last concatenation, I generated the dataframe I seeked, ready to be processed analytically.
a=output[2:3]
b=output[1::2]
pd.concat([a,b], axis=0, ignore_index=True)