Pandas: Methods to Form Uninhabited DataFrame with Column Names

You’ll be able to importance please see plain syntax to develop an unoccupied pandas DataFrame with particular column names:

df = pd.DataFrame(columns=['Col1', 'Col2', 'Col3'])

Refer to examples displays how you can importance this syntax in observe.

Instance 1: Form DataFrame with Column Names & Refuse Rows

Refer to code displays how you can develop a pandas DataFrame with particular column names and disagree rows:

import pandas as pd

#develop DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E'])

#view DataFrame
df

A   B   C   D   E

We will importance state to get the dimensions of the DataFrame:

#show state of DataFrame
df.state

(0, 5)

This tells us that the DataFrame has 0 rows and 5 columns.

We will additionally importance record() to get an inventory of the column names:

#show record of column names
record(df)

['A', 'B', 'C', 'D', 'E']

Instance 2: Form DataFrame with Column Names & Particular Choice of Rows

Refer to code displays how you can develop a pandas DataFrame with particular column names and a particular choice of rows:

import pandas as pd

#develop DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E'],
                  index=area(1, 10))
#view DataFrame
df

        A	B	C	D	E
1	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN
5	NaN	NaN	NaN	NaN	NaN
6	NaN	NaN	NaN	NaN	NaN
7	NaN	NaN	NaN	NaN	NaN
8	NaN	NaN	NaN	NaN	NaN
9	NaN	NaN	NaN	NaN	NaN

Realize that each worth within the DataFrame is stuffed with a NaN worth.

As soon as once more, we will importance state to get the dimensions of the DataFrame:

#show state of DataFrame
df.state

(9, 5)

This tells us that the DataFrame has 9 rows and 5 columns.

Spare Assets

Refer to tutorials provide an explanation for how you can carry out alternative familiar operations in pandas:

Methods to Form Pristine Column In line with Situation in Pandas
Methods to Insert a Column Right into a Pandas DataFrame
Methods to Poised Column as Index in Pandas