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Pandas is designed to load a fully populated DataFrame. We can pandas.DataFrameadd them one by one in . This can be done by using various methods, such as .loc, dictionary, pandas.concat()or DataFrame.append().

.loc [index]Add rows to a Pandas DataFrame with a list using

loc[index]The new list is treated as a new row and added to pandas.dataframeindex indexof .

Consider the following code:

# python 3.x
import pandas as pd

# List of Tuples
fruit_list = [("Orange", 34, "Yes")]
# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
# Add new ROW
df.loc[1] = ["Mango", 4, "No"]
df.loc[2] = ["Apple", 14, "Yes"]
print(df)

result:

     Name  Price Stock
0  Orange     34   Yes
1   Mango      4    No
2   Apple     14   Yes

Add dictionary as row to Pandas DataFrame

append()You can add the dictionary keys and values ​​directly to a pandas dataframe as a row.

Consider the following code:

# python 3.x
import pandas as pd

# List of Tuples
fruit_list = [("Orange", 34, "Yes")]
# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
# Add new ROW
df = df.append({"Name": "Apple", "Price": 23, "Stock": "No"}, ignore_index=True)
df = df.append({"Name": "Mango", "Price": 13, "Stock": "Yes"}, ignore_index=True)
print(df)

result:

     Name  Price Stock
0  Orange     34   Yes
1   Apple     23    No
2   Mango     13   Yes

Dataframe .appendmethod to add a row

.appendCan be used to DataFrameappend the rows of an additional to DataFramethe end of an original , and return a new DataFrame. DataFrameColumns from the new (not datafarmein the original ) are also added to the existing DataFrame, and the new cell values ​​are filled in NaN.

Consider the following code:

# python 3.x
import pandas as pd

# List of Tuples
fruit_list = [("Orange", 34, "Yes")]
# Create a DataFrame object
df = pd.DataFrame(fruit_list, columns=["Name", "Price", "Stock"])
print("Original DataFrame:")
print(df)
print(".............................")
print(".............................")
new_fruit_list = [("Apple", 34, "Yes", "small")]
dfNew = pd.DataFrame(new_fruit_list, columns=["Name", "Price", "Stock", "Type"])
print("Newly Created DataFrame:")
print(dfNew)
print(".............................")
print(".............................")
# append one dataframe to othher
df = df.append(dfNew, ignore_index=True)
print("Copying DataFrame to orignal...")
print(df)

ignore_index = TrueDataFrameThe new is ignored indexand DataFrameassigned a new index from the original .

Output:

Original DataFrame:
     Name  Price Stock
0  Orange     34   Yes
.............................
.............................
Newly Created DataFrame:
    Name  Price Stock   Type
0  Apple     34   Yes  small
.............................
.............................
Copying  DataFrame to original..:
     Name  Price Stock   Type
0  Orange     34   Yes    NaN
1   Apple     34   Yes  small