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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