Convert row to column header for Pandas DataFrame,

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The data I have to work with is a bit messy.. It has header names inside of its data. How can I choose a row from an existing pandas dataframe and make it (rename it to) a column header?

I want to do something like:

header = df[df['old_header_name1'] == 'new_header_name1']

df.columns = header

In [21]: df = pd.DataFrame([(1,2,3), ('foo','bar','baz'), (4,5,6)])

In [22]: df
Out[22]: 
     0    1    2
0    1    2    3
1  foo  bar  baz
2    4    5    6

Set the column labels to equal the values in the 2nd row (index location 1):

In [23]: df.columns = df.iloc[1]

If the index has unique labels, you can drop the 2nd row using:

In [24]: df.drop(df.index[1])
Out[24]: 
1 foo bar baz
0   1   2   3
2   4   5   6

If the index is not unique, you could use:

In [133]: df.iloc[pd.RangeIndex(len(df)).drop(1)]
Out[133]: 
1 foo bar baz
0   1   2   3
2   4   5   6

Using df.drop(df.index[1]) removes all rows with the same label as the second row. Because non-unique indexes can lead to stumbling blocks (or potential bugs) like this, it's often better to take care that the index is unique (even though Pandas does not require it).

Convert row to column header for Pandas DataFrame, Let us do it with the help of an example: df = pd.DataFrame([(1,2,3), ('cricket','​football','hockey'), (4,5,6)]). df. Out: 0 1 2. 0 1 2 3. 1 cricket football  The data I have to work with is a bit messy.. It has header names inside of its data. How can I choose a row from an existing pandas dataframe and make it (rename it to) a column header? I want to do something like: header = df[df['old_header_name1'] == 'new_header_name1'] df.columns = header


This works (pandas v'0.19.2'):

df.rename(columns=df.iloc[0])

How to convert a Pandas DataFrame row to column headers in Python, Use pandas. DataFrame. columns to convert a row to column headers. df. columns = df. iloc[header_row] Assign row as column headers. df = df. drop(header_row) Drop header row. df = df. reset_index(drop=True) Reset index. #Convert to a DataFrame and render. import pandas as pd #Save the dataset in a variable df = pd.DataFrame.from_records (rows) # Lets see the 5 first rows of the dataset df.head () Then, run the next bit of code: # Create a new variable called 'new_header' from the first row of. # This calls the first row for the header new_header = df.iloc [0]


It would be easier to recreate the data frame. This would also interpret the columns types from scratch.

headers = df.iloc[0]
new_df  = pd.DataFrame(df.values[1:], columns=headers)

How to make the first row in your spreadsheet or dataframe, #Convert to a DataFrame and render. import pandas as pd #Save the dataset set the header row as the df header df.columns = new_header I have the following code, which takes the values in one column of a pandas dataframe and makes them the columns of a new data frame. The values in the first column of the dataframe become the index of the new dataframe. In a sense, I want to turn an adjacency list into an adjacency matrix. Here's the code so far:


You can specify the row index in the read_csv or read_html constructors via the header parameter which represents Row number(s) to use as the column names, and the start of the data. This has the advantage of automatically dropping all the preceding rows which supposedly are junk.

import pandas as pd
from io import StringIO

In[1]
    csv = '''junk1, junk2, junk3, junk4, junk5
    junk1, junk2, junk3, junk4, junk5
    pears, apples, lemons, plums, other
    40, 50, 61, 72, 85
    '''

    df = pd.read_csv(StringIO(csv), header=2)
    print(df)

Out[1]
       pears   apples   lemons   plums   other
    0     40       50       61      72      85

pandas.DataFrame.transpose, Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the  In order to convert a column to row name/index in dataframe, Pandas has a built-in function Pivot. Now, let’s say we want Result to be the rows/index, and columns be name in our dataframe, to achieve this pandas has provided a method called Pivot. Let us see how it works, # importing pandas as pd. import pandas as pd. # Creating a dict of lists.


Reshaping and pivot tables, In [3]: df.pivot(index='date', columns='variable', values='value') Out[3]: MultiIndex.from_tuples(tuples, names=['first', 'second']) In [10]: df = pd. For integer types, by default data will converted to float and missing values will be set to NaN . Replace the header value with the first row’s values. # Create a new variable called 'header' from the first row of the dataset header = df.iloc[0] 0 first_name 1 last_name 2 age 3 preTestScore Name: 0, dtype: object. # Replace the dataframe with a new one which does not contain the first row df = df[1:] # Rename the dataframe's column values


Convert a column to row name/index in Pandas, set the index to 'None' via its name property. df.index.names = [ None ]. df In order to convert a column to row name/index in dataframe, Pandas has a built-in​  So my dataset has some information by location for n dates. The problem is each date is actually a different column header. For example the CSV looks like. You can use pd.melt to get most of the way there, and then sort: (Might want to throw in a .reset_index (drop=True), just to keep the output clean.) Note: pd.DataFrame.sort has been


Rename Column Headers In pandas, Import required modules import pandas as pd Replace the header value with the first row's values. # Create a Rename the dataframe's column values with the header variable Head to and submit a suggested change. I currently have a dataframe that looks like this: I'm looking for a way to delete the header row and make the first row the new header row, so the new dataframe would look like this: I've tried stuff along the lines of if 'Unnamed' in df.columns: then make the dataframe without the header df.to_csv (newformat,header=False,index=False) but I