Pandas Append Rows & Columns to Empty DataFrame

You can find out how to create an empty pandas DataFrame and append rows and columns to it by using DataFrame.append() method and DataFrame.loc[] property. In this article, I will explain how to append a row and column to empty DataFrame by several methods.

1. Quick Examples to Append Empty DataFrame

If you are in hurry below are some quick examples to append rows and columns to an empty DataFrame in pandas.


# Below are some quick examples.
# Create a empty DataFrame.
df = pd.DataFrame()

# Append columns to an empty DataFrame.
df['Courses'] = ['Spark', 'PySpark', 'Python']
df['Fee'] = [15000, 20000, 25000]
df['Duration'] = ['30days','35days','50days']

# Append Rows to Empty DataFrame.
df2 = df.append({'Courses' : 'Spark', 'Fee' : 15000, 'Discount' : '30days'}, ignore_index = True)

# Create DataFrame with Column name and indices using loc[] property.
df = pd.DataFrame(columns = ['Courses', 'Fee', 'Duration'], 
                   index = ['1', '2', '3'])
df.loc['1'] = ['Courses',15000,'30days']
print(df)

2. Append Columns to Empty DataFrame

First, let’s create an empty pandas DataFrame without any column names or indices and then append columns one by one to it.


# Create a empty DataFrame.
df = pd.DataFrame()
print(df)

# Append columns to an empty DataFrame.
df['Courses'] = ['Spark', 'PySpark', 'Python']
df['Fee'] = [15000, 20000, 25000]
df['Duration'] = ['30days','35days','50days']

Yields below output.


Empty DataFrame
Columns: []
Index: []
   Courses    Fee Duration
0    Spark  15000   30days
1  PySpark  20000   35days
2   Python  25000   50days

3. Append Rows to Empty DataFrame

pandas.DataFrame.append() function is used to add the rows of other DataFrame to the end of the given DataFrame and return a new DataFrame object.


# Append Rows to Empty DataFrame.
df2 = df.append({'Courses' : 'Spark', 'Fee' : 15000, 'Discount' : '30days'}, ignore_index = True)
print(df2)             

Yields below output.


Empty DataFrame
Columns: [Courses, Fee, Duration]
Index: []
  Courses    Fee Duration Discount
0   Spark  15000      NaN   30days

4. By using loc[] to Append Row

You can find out how to create an empty DataFrame with column names and indices and then append rows one by one to it using DataFrame.loc[] property. The loc[] property is used to access a group of rows and columns by label(s) or a boolean array.


# Create DataFrame with Column name and indices using loc[] property.
df = pd.DataFrame(columns = ['Courses', 'Fee', 'Duration'], 
                   index = ['1', '2', '3'])
df.loc['1'] = ['Courses',15000,'30days']
print(df)

Yields below output.


Empty DataFrame
Columns: []
Index: []
   Courses    Fee Duration
1  Courses  15000   30days
2      NaN    NaN      NaN
3      NaN    NaN      NaN

5. Complete Example of Append Rows & Columns to Empty DataFrame


# Create a empty DataFrame.
df = pd.DataFrame()

# Append columns to an empty DataFrame.
df['Courses'] = ['Spark', 'PySpark', 'Python']
df['Fee'] = [15000, 20000, 25000]
df['Duration'] = ['30days','35days','50days']
print(df)

# Append Rows to Empty DataFrame.
df2 = df.append({'Courses' : 'Spark', 'Fee' : 15000, 'Discount' : '30days'}, ignore_index = True)
print(df2)

# Create DataFrame with Column name and indices using loc[] property.
df = pd.DataFrame(columns = ['Courses', 'Fee', 'Duration'], 
                   index = ['1', '2', '3'])
df.loc['1'] = ['Courses',15000,'30days']
print(df)

Conclusion

In this article, You have learned how to append rows and columns and indices using DataFrame.append() and DataFrame.loc[] property with multiple examples.

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