I wonder how to save a new pandas Series into a csv file in a different column. Suppose I have two csv files which both contains a column as a 'A'. I have done some mathematical function on them and then create a new variable as a 'B'.
For example:
data = pd.read_csv('filepath')
data['B'] = data['A']*10
# and add the value of data.B into a list as a B_list.append(data.B)
This will continue until all of the rows of the first and second csv file has been reading.
I would like to save a column B in a new spread sheet from both csv files.
For example I need this result:
colum1(from csv1) colum2(from csv2)
data.B.value data.b.value
By using this code:
pd.DataFrame(np.array(B_list)).T.to_csv('file.csv', index=False, header=None)
I won't get my preferred result.
Since each column in a pandas DataFrame is a pandas Series. Your B_list is actually a list of pandas Series which you can cast to DataFrame() constructor, then transpose (or as #jezrael shows a horizontal merge with pd.concat(..., axis=1))
finaldf = pd.DataFrame(B_list).T
finaldf.to_csv('output.csv', index=False, header=None)
And should csv have different rows, unequal series are filled with NANs at corresponding rows.
I think you need concat column from data1 with column from data2 first:
df = pd.concat(B_list, axis=1)
df.to_csv('file.csv', index=False, header=None)
Related
I am reading from an Excel file ".xslx", it's consist of 3 columns, but when I read from it, I get a DF full of nans, I checked the table in Excel, it consists of normal cells no formulas no hyperlinks.
My code:
data = pd.read_excel("Data.xlsx")
df = pd.DataFrame(data, columns=["subreddit_group", "links/caption", "subreddits/flair"])
print(df)
Here is the excel file:
Here is the output:
The column parameter of pd.Dataframe() function doesn't set column names in result dataframe, but selects columns from the original file.
See pandas documentation :
Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, …, n). If data contains column labels, will perform column selection instead.
So you shouldn't provide column parameter and after the file is read, rename columns of the dataframe:
df = pd.DataFrame(data)
df.columns = ['subreddit_group', 'links/caption', 'def']
I have a csv file with a wrong first row data. The names of labels are in the row number 2. So when I am storing this file to the DataFrame the names of labels are incorrect. And correct names become values of the row 0. Is there any function similar to reset_index() but for columns? PS I can not change csv file. Here is an image for better understanding. DataFrame with wrong labels
Hello let's suppose you csv file is data.csv :
Try this code:
import pandas as pd
#reading the csv file
df = pd.read_csv('data.csv')
#changing the headers name to integers
df.columns = range(df.shape[1])
#saving the data in another csv file
df.to_csv('data_without_header.csv',header=None,index=False)
#reading the new csv file
new_df = pd.read_csv('data_without_header.csv')
#plotting the new data
new_df.head()
If you do not care about the rows preceding your column names, you can pass in the "header" argument with the value of the correct row, for example if the proper column names are in row 2:
df = pd.read_csv('my_csv.csv', header=2)
Keep in mind that this will erase the previous rows from the DataFrame. If you still want to keep them, you can do the following thing:
df = pd.read_csv('my_csv.csv')
df.columns = df.iloc[2, :] # replace columns with values in row 2
Cheers.
Suppose there is a Excel file:
Is there a way to read it directly as a Pandas dataframe with multiindex, without filling blank spaces in the first column?
Data:
Code:
df = pd.read_excel('test.xlsx')
.ffill():
df.i0.ffill(inplace=True)
set_index():
df.set_index(['i0', 'i1'], inplace=True)
I'm trying to read an Excel or CSV file into pandas dataframe. The file will read the first two columns only, and the top row of the first two columns will be the column names. The problem is when I have the first column of the top row empty in the Excel file.
IDs
2/26/2010 2
3/31/2010 4
4/31/2010 2
5/31/2010 2
Then, the last line of the following code fails:
uploaded_file = request.FILES['file-name']
if uploaded_file.name.endswith('.csv'):
df = pd.read_csv(uploaded_file, usecols=[0,1])
else:
df = pd.read_excel(uploaded_file, usecols=[0,1])
ref_date = 'ref_date'
regime_tag = 'regime_tag'
df.columns = [ref_date, regime_tag]
Apparently, it only reads one column (i.e. the IDs). However, with read_csv, it reads both column, with the first column being unnamed. I want it to behave that way and read both columns regardless of whether the top cells are empty or filled. How do I go about doing that?
What's happening is the first "column" in the Excel file is being read in as an index, while in the CSV file it's being treated as a column / series.
I recommend you work the other way and amend pd.read_csv to read the first column as an index. Then use reset_index to elevate the index to a series:
if uploaded_file.name.endswith('.csv'):
df = pd.read_csv(uploaded_file, usecols=[0,1], index_col=0)
else:
df = pd.read_excel(uploaded_file, header=[0,1], usecols=[0,1])
df = df.reset_index() # this will elevate index to a column called 'index'
This will give consistent output, i.e. first series will have label 'index' and the index of the dataframe will be the regular pd.RangeIndex.
You could potentially use a dispatcher to get rid of the unwieldy if / else construct:
file_flag = {True: pd.read_csv, False: pd.read_excel}
read_func = file_flag[uploaded_file.name.endswith('.csv')]
df = read_func(uploaded_file, usecols=[0,1], index_col=0).reset_index()
I need to read a csv file in python and then re arrange the columns of csv and make a new dataframe made of the rearranged columns
I tried using list, but it might work slow..
Any alternative using numpy or pandas?
Edit:
I am rearranging the row using df.reindex()
I am currently doing this and thus exporting the df after leaving 4 rows blank
df_reindexed.to_excel(writer, sheet_name='Sheet1',startrow=4, index=False)
I need to add format and text to cells in those top 4 rows, corresponding to the column name in the following rows.
I know I can use iloc, but is there anyway to do it so that i can select a cell above a cell with specified name?
import pandas as pd
# read a CSV with pandas
src = "your/path"
old_df = pd.read_csv(src, sep=",")
# the columns that you want
desired_cols = ['c1','c2']
# pandas will return a new df only with the columns that you want
new_df = old_df[desired_cols]
Another way to do it is:
desired_cols = ['c1', 'c2', 'c3']
df_final = df_final.reindex(columns = desired_cols)