CODEWITHSUNDEEP
pythonpandasdataframe
E. Muuli
E. Muuli

Reputation: 4340

Pandas create empty DataFrame with only column names

I have a dynamic DataFrame which works fine, but when there are no data to be added into the DataFrame I get an error. And therefore I need a solution to create an empty DataFrame with only the column names.

For now I have something like this:

df = pd.DataFrame(columns=COLUMN_NAMES) # Note that there is no row data inserted.

PS: It is important that the column names would still appear in a DataFrame.

But when I use it like this I get something like that as a result:

Index([], dtype='object')
Empty DataFrame

The "Empty DataFrame" part is good! But instead of the Index thing I need to still display the columns.

An important thing that I found out: I am converting this DataFrame to a PDF using Jinja2, so therefore I'm calling out a method to first output it to HTML like that:

df.to_html()

This is where the columns get lost I think.

In general, I followed this example: http://pbpython.com/pdf-reports.html. The css is also from the link. That's what I do to send the dataframe to the PDF:

env = Environment(loader=FileSystemLoader('.'))
template = env.get_template("pdf_report_template.html")
template_vars = {"my_dataframe": df.to_html()}

html_out = template.render(template_vars)
HTML(string=html_out).write_pdf("my_pdf.pdf", stylesheets=["pdf_report_style.css"])

Upvotes: 359

Views: 860899

Answers (5)

cottontail
cottontail

Reputation: 23111

If you have a completely empty dataframe without columns or index, you can let it have columns by assigning None to these columns.

df = pd.DataFrame()                    # <---- shape: (0, 0)
df[['col1', 'col2', 'col3']] = None    # <---- shape: (0, 3)

Then to assign a row to it, you can use loc indexer. This can actually be used in a loop to add more rows (something that's inadvisable as pd.concat exists to do that particular task).

df.loc[len(df)] = ['abc', 10, 3.33]    # <---- shape: (1, 3)

res

Upvotes: 1

bitbang
bitbang

Reputation: 2182

Creating colnames with iterating

df = pd.DataFrame(columns=['colname_' + str(i) for i in range(5)])
print(df)

# Empty DataFrame
# Columns: [colname_0, colname_1, colname_2, colname_3, colname_4]
# Index: []

to_html() operations

print(df.to_html())

# <table border="1" class="dataframe">
#   <thead>
#     <tr style="text-align: right;">
#       <th></th>
#       <th>colname_0</th>
#       <th>colname_1</th>
#       <th>colname_2</th>
#       <th>colname_3</th>
#       <th>colname_4</th>
#     </tr>
#   </thead>
#   <tbody>
#   </tbody>
# </table>

this seems working

print(type(df.to_html()))
# <class 'str'>

The problem is caused by

when you create df like this

df = pd.DataFrame(columns=COLUMN_NAMES)

it has 0 rows × n columns, you need to create at least one row index by

df = pd.DataFrame(columns=COLUMN_NAMES, index=[0])

now it has 1 rows × n columns. You are be able to add data. Otherwise its df that only consist colnames object(like a string list).

Upvotes: 18

Eric
Eric

Reputation: 65

df.to_html() has a columns parameter.

Just pass the columns into the to_html() method.

df.to_html(columns=['A','B','C','D','E','F','G'])

Upvotes: 3

Marcus V.
Marcus V.

Reputation: 6859

You can create an empty DataFrame with either column names or an Index:

In [4]: import pandas as pd
In [5]: df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
In [6]: df
Out[6]:
Empty DataFrame
Columns: [A, B, C, D, E, F, G]
Index: []

Or

In [7]: df = pd.DataFrame(index=range(1,10))
In [8]: df
Out[8]:
Empty DataFrame
Columns: []
Index: [1, 2, 3, 4, 5, 6, 7, 8, 9]

Edit: Even after your amendment with the .to_html, I can't reproduce. This:

df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
df.to_html('test.html')

Produces:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
      <th>D</th>
      <th>E</th>
      <th>F</th>
      <th>G</th>
    </tr>
  </thead>
  <tbody>
  </tbody>
</table>

Upvotes: 447

Linda
Linda

Reputation: 647

Are you looking for something like this?

    COLUMN_NAMES=['A','B','C','D','E','F','G']
    df = pd.DataFrame(columns=COLUMN_NAMES)
    df.columns

   Index(['A', 'B', 'C', 'D', 'E', 'F', 'G'], dtype='object')

Upvotes: 22

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