I want to add multiple empty rows at start of my dataframe. I have tried using list but it dosen't seem to return optimum result:
Example df:
Col1
col2
col3
col4
One
Two
Three
four
2
4
5
8
Desired df:
Col1
col2
col3
col4
One
Two
Three
four
2
4
5
8
Column names should also start from the nth row, I want to add n empty rows at the beginning of my Dataframe.
I'm not sure why you would want to do this but I did it by splitting up the original dataframe into a dataframe with a row of the column names and a separate dataframe of the data. I then created a dataframe of nans to be the blank rows and joined the 3 together. You will need to import numpy for this.
I created a variable no_cols to be the number of columns in the dataframe and no_empty_rows to be how many empty rows to simplify code:
no_cols = len(df.columns)
no_empty_rows = 6
Then I turned the columns into their own dataframe, with 1 row which is the column names, and headers as np.nan:
cols = pd.DataFrame([df.columns], columns = [np.nan]*no_cols)
NaN NaN NaN NaN
0 Col1 col2 col3 col4
Next I renamed the columns in the original dataframe to nan:
df.columns = [np.nan]*no_cols
NaN NaN NaN NaN
0 One Two Three four
1 2 4 5 8
Then I created a new dataframe of nans, with 6 blank rows (this can be changed):
df_empty_rows = (pd.DataFrame(data=[[np.nan]*no_cols]*no_empty_rows,
columns=[np.nan]*no_cols,
index=[np.nan]*no_empty_rows))
NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
You can then append together all 3. First I put the columns and data of df back together and reset their index, then append that to df_empty_rows:
df_out = df_empty_rows.append(cols.append(df).reset_index(drop=True))
NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN
0.0 Col1 col2 col3 col4
1.0 One Two Three four
2.0 2 4 5 8
Full code:
no_cols = len(df.columns)
no_empty_rows = 6
cols = pd.DataFrame([df.columns], columns=[np.nan]*no_cols)
df.columns = [np.nan]*no_cols
df_empty_rows = (pd.DataFrame(data=[[np.nan]*no_cols]*no_empty_rows,
columns=[np.nan]*no_cols,
index=[np.nan]*no_empty_rows))
df_out = df_empty_rows.append(cols.append(df).reset_index(drop=True))
Related
I have a excel with multiple sheets in the below format. I need to create a single dataframe by concatenating all the sheets, unmerging the cell and then transposing them into a column based on the sheet
Sheet 1:
Sheet 2:
Final Dataframe should look like below
Result expected - I need the below format with an extra coulmn as below
Code So far:
Reading File:
df = pd.concat(pd.read_excel('/Users/john/Desktop/Internal/Raw Files/Med/Dig/File_2017_2022.xlsx', sheet_name=None, skiprows=1))
Creating Column :
df_1 = pd.concat([df.assign(name=n) for n,df in dfs.items()])
Use read_excel with header=[0,1] for MultiIndex by first 2 rows of header and index_col=[0,1] for MultiIndex by first 2 columns, so possible in loop reshape by DataFrame.stack, add new column, use concat and last set index names by DataFrame.rename_axis with convert to columns by DataFrame.reset_index:
dfs = pd.read_excel('Input_V1.xlsx',sheet_name=None, header=[0,1], index_col=[0,1])
df_1 = (pd.concat([df.stack(0).assign(name=n) for n,df in dfs.items()])
.rename_axis(index=['Date','WK','Brand'], columns=None)
.reset_index())
df_1.insert(len(df_1.columns) - 2, 'Campaign', df_1.pop('Campaign'))
print (df_1)
Date WK Brand A B C D E F G \
0 2017-10-02 Week 40 ABC NaN NaN NaN NaN 56892.800000 83431.664000 NaN
1 2017-10-09 Week 41 ABC NaN NaN NaN NaN 0.713716 0.474025 NaN
2 2017-10-16 Week 42 ABC NaN NaN NaN NaN 0.025936 0.072500 NaN
3 2017-10-23 Week 43 ABC NaN NaN NaN NaN 0.182677 0.926731 NaN
4 2017-10-30 Week 44 ABC NaN NaN NaN NaN 0.755607 0.686115 NaN
.. ... ... ... .. .. .. .. ... ... ..
99 2018-03-26 Week 13 PQR NaN NaN NaN NaN 47702.000000 12246.000000 NaN
100 2018-04-02 Week 14 PQR NaN NaN NaN NaN 38768.000000 46498.000000 NaN
101 2018-04-09 Week 15 PQR NaN NaN NaN NaN 35917.000000 45329.000000 NaN
102 2018-04-16 Week 16 PQR NaN NaN NaN NaN 39639.000000 51343.000000 NaN
103 2018-04-23 Week 17 PQR NaN NaN NaN NaN 50867.000000 30119.000000 NaN
H I J K L Campaign name
0 NaN NaN NaN 0.017888 0.697324 NaN ABC
1 NaN NaN NaN 0.457963 0.810985 NaN ABC
2 NaN NaN NaN 0.743030 0.253668 NaN ABC
3 NaN NaN NaN 0.038683 0.050028 NaN ABC
4 NaN NaN NaN 0.885567 0.712333 NaN ABC
.. .. .. .. ... ... ... ...
99 NaN NaN NaN 9433.000000 17108.000000 WX PQR
100 NaN NaN NaN 12529.000000 23557.000000 WX PQR
101 NaN NaN NaN 20395.000000 44228.000000 WX PQR
102 NaN NaN NaN 55077.000000 45149.000000 WX PQR
103 NaN NaN NaN 45815.000000 35761.000000 WX PQR
[104 rows x 17 columns]
I created my own version of your excel, which looks like
this
The code below is far from perfect but it should do fine as long as you do not have millions of sheets
# First, obtain all sheet names
full_df = pd.read_excel(r'C:\Users\.\Downloads\test.xlsx',
sheet_name=None, skiprows=0)
# Store them into a list
sheet_names = list(full_df.keys())
# Create an empty Dataframe to store the contents from each sheet
final_df = pd.DataFrame()
for sheet in sheet_names:
df = pd.read_excel(r'C:\Users\.\Downloads\test.xlsx', sheet_name=sheet, skiprows=0)
# Get the brand name
brand = df.columns[1]
# Remove the header columns and keep the numerical values only
df.columns = df.iloc[0]
df = df[1:]
df = df.iloc[:, 1:]
# Set the brand name into a new column
df['Brand'] = brand
# Append into the final dataframe
final_df = pd.concat([final_df, df])
Your final_df should look like this once exported back to excel
EDIT: You might need to drop the dataframe's index upon saving it by using the df.reset_index(drop=True) function, to remove the first column shown in the image right above.
I am writing a script to scrape a series of tables in a pdf into python using tabula-py.
This is fine. I do get the data. But the data is multi-line, and useless in reality.
I would like to merge the rows where the first column (Tag is not NaN).
I was about to put the whole thing in an iterator, and do it manually, but I realize that pandas is a powerful tool, but I don't have the pandas vocabulary to search for the right tool. Any help is much appreciated.
My Code
filename='tags.pdf'
tagTableStart=2 #784
tagTableEnd=39 #822
tableHeadings = ['Tag','Item','Length','Description','Value']
pageRange = "%d-%d" % (tagTableStart, tagTableEnd)
print ("Scanning pages %s" % pageRange)
# extract all the tables in that page range
tables = tabula.read_pdf(filename, pages=pageRange)
How The data is stored in the DataFrame:
(Empty fields are NaN)
Tag
Item
Length
Description
Value
AA
Some
2
Very Very
Text
Very long
Value
AB
More
4
Other Very
aaaa
Text
Very long
bbbb
Value
cccc
How I want the data:
This is almost as it is displayed in the pdf (I couldn't figure out how to make text multi line in SO editor)
Tag
Item
Length
Description
Value
AA
Some\nText
2
Very Very\nVery long\nValue
AB
More\nText
4
Other Very\nVery long\n Value
aaaa\nbbbb\ncccc
Actual sample output (obfuscated)
Tag Item Length Description Value
0 AA PYTHROM-PARTY-I 20 Some Current defined values are :
1 NaN NaN NaN texst Byte1:
2 NaN NaN NaN NaN C
3 NaN NaN NaN NaN DD
4 NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN DD
6 NaN NaN NaN NaN DD
7 NaN NaN NaN NaN DD
8 NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN B :
10 NaN NaN NaN NaN JLSAFISFLIHAJSLIhdsflhdliugdyg89o7fgyfd
11 NaN NaN NaN NaN ISFLIHAJSLIhdsflhdliugdyg89o7fgyfd
12 NaN NaN NaN NaN upon ISFLIHAJSLIhdsflhdliugdyg89o7fgy
13 NaN NaN NaN NaN asdsadct on the dasdsaf the
14 NaN NaN NaN NaN actsdfion.
15 NaN NaN NaN NaN NaN
16 NaN NaN NaN NaN SLKJDBFDLFKJBDSFLIUFy7dfsdfiuojewv
17 NaN NaN NaN NaN csdfgfdgfd.
18 NaN NaN NaN NaN NaN
19 NaN NaN NaN NaN fgfdgdfgsdfgfdsgdfsgfdgfdsgsdfgfdg
20 BB PRESENT-AMOUNT-BOX 11 Lorem Ipsum NaN
21 CC SOME-OTHER-VALUE 1 sdlkfgsdsfsdf 1
22 NaN NaN NaN device NaN
23 NaN NaN NaN ueghkjfgdsfdskjfhgsdfsdfkjdshfgsfliuaew8979vfhsdf NaN
24 NaN NaN NaN dshf87hsdfe4ir8hod9 NaN
Create groups from ID columns then join each rows:
agg_func = dict(zip(df.columns, [lambda s: '\n'.join(s).strip()] * len(df.columns)))
out = df.fillna('').groupby(df['Tag'].ffill(), as_index=False).agg(agg_func)
Output:
>>> out
Tag Item Length Description Value
0 AA Some\nText 2 Very Very\nVery long\nValue
1 AB More\nText 4 Other Very\nVery long\nValue aaaa\nbbbb\ncccc
agg_func is equivalent to write:
{'Tag': lambda s: '\n'.join(s).strip(),
'Item': lambda s: '\n'.join(s).strip(),
'Length': lambda s: '\n'.join(s).strip(),
'Description': lambda s: '\n'.join(s).strip(),
'Value': lambda s: '\n'.join(s).strip()}
I have a list of values [0.1, 0.43, 0.58] and a dataframe df with several columns. I added three new columns in my dataframe with NaN values, and I want to replace them with the ones from the list. Each list value split into each new column in that exact order.
The dataframe is 4 columns (no index shown), with 3 new columns.
Name A B C New1 New2 New3
Elem1 NaN NaN NaN NaN NaN NaN
Elem2 NaN NaN NaN NaN NaN NaN
Elem3 NaN NaN NaN NaN NaN NaN
Expected result:
Name A B C New1 New2 New3
Elem1 NaN NaN NaN NaN NaN NaN
Elem2 NaN NaN NaN 0.1 0.43 0.58
Elem3 NaN NaN NaN NaN NaN NaN
If l is your list, then:
df.loc[df.Name=='Elem2', 'New1':'New3'] = l
I am trying to clean np values in an open sourced data.
I am using python3, Jupyter and pandas.
response = urllib.request.urlopen('https://resources.lendingclub.com/LoanStats3c.csv.zip')
import shutil
url = 'https://resources.lendingclub.com/LoanStats3c.csv.zip'
file_name = 'LoanStats3c.csv.zip'
with urllib.request.urlopen(url) as response, open(file_name, 'wb') as out_file:
shutil.copyfileobj(response, out_file)
with zipfile.ZipFile(file_name) as zf:
zf.extractall()
loan=pd.read_csv(open('LoanStats3c.csv'), skiprows=1, parse_dates=True, index_col='id')
loan.describe()
# remove all columns with all NAs
loan = loan.dropna(axis=1, how = 'all')
loan.describe()
# remove all rows with any NAs
loan = loan.dropna(axis = 0)
loan.describe()
But, the results are all columns with all NAs:
loan_amnt funded_amnt funded_amnt_inv installment annual_inc dti \
count 0.0 0.0 0.0 0.0 0.0 0.0
mean NaN NaN NaN NaN NaN NaN
std NaN NaN NaN NaN NaN NaN
min NaN NaN NaN NaN NaN NaN
25% NaN NaN NaN NaN NaN NaN
50% NaN NaN NaN NaN NaN NaN
75% NaN NaN NaN NaN NaN NaN
max NaN NaN NaN NaN NaN NaN
Why all rows with valid values are gone and only the NA columns are left ?
thanks
When you'r using .dropna() like that all ocurrences with NaN values are deleted from dataframe
loan.dropna(axis=1, how = 'all')
Will delete the columns with all values in NaN
loan.dropna(axis = 0)
Will delete the rows with a least one value in NaN
I saw the file and i'm pretty sure that every rows has at least one column in NaN
Finally when using .describe() the dataframe is empty and the values that are shown are a descriptive statistics of that, if you want to see the real DF use print(df) or in jupyter just let the variable at the end of the block
some code
some code
some code
variable = pd.DataFrame([])
#print(variable)
variable
That would show you the value of the variable
I am having some issues on how to sort columns based on the last row getting a 'KeyError: -1'
I have tried df.sort_values(by=[-1], axis=1,na_position='last') and
df.sort_values(by=df[-1], axis=1,na_position='last')
timestamp AKS AGI AA ATI ... TK TNP USDP ZTO
2019-09-10 NaN NaN NaN NaN ... 0.063570 0.057432 -0.121778 0.098429
2019-09-11 NaN NaN NaN NaN ... 0.083130 0.043919 -0.128889 0.104712
2019-09-12 NaN NaN NaN NaN ... 0.080685 0.047297 -0.130667 0.135079
2019-09-13 NaN NaN NaN NaN ... 0.090465 0.020270 -0.123556 0.112565
2019-09-16 NaN NaN NaN NaN ... NaN NaN NaN NaN
some code
sorted_df = df.sort_values(by=[-1], axis=1,na_position='last')
Expected results to be that the columns are sorted by the last row
Well if you want to know what you last column is, you can do that by using
df.columns.tolist()[-1]
So if you want to sort a df by the last column it turns out as
df.sort_values(by=df.columns.tolist()[-1], ascending = False)
and then you can na_position
na_position : {‘first’, ‘last’}, default ‘last’
first puts NaNs at the beginning, last puts NaNs at the end