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.
Related: In Pandas, you can append DataFrame using for loop.
1. Quick Examples to Append Empty DataFrame
If you are in a 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.
# 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.
# 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.
# 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, columns, and indices using DataFrame.append() and DataFrame.loc[] property with multiple examples.
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