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#This was first Posted By Hilarious Hornet
newDF = pd.DataFrame() #creates a new dataframe that's empty
newDF = newDF.append(oldDF, ignore_index = True) # ignoring index is optional
# try printing some data from newDF
print(newDF.head()) #again optional
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# Basic syntax:
import pandas as pd
empty_dataframe = pd.DataFrame()
# Create empty dataframe with column names
empty_dataframe = pd.DataFrame(columns=['your', 'column', 'names'])
# Create empty dataframe with row names
empty_dataframe = pd.DataFrame(index=['your', 'row', 'names'])
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newDF = pd.DataFrame() #creates a new dataframe that's empty
newDF = newDF.append(oldDF, ignore_index = True) # ignoring index is optional
# try printing some data from newDF
print newDF.head() #again optional
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import pandas as pd
df = pd.DataFrame(columns=['A','B','C','D','E','F','G'])
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import pandas as pd
col_names = ['A', 'B', 'C']
my_df = pd.DataFrame(columns = col_names)
my_df
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>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True
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#Create empty DataFrame with specific column names & types
df = pd.DataFrame({'Courses': pd.Series(dtype='str'),
'Fee': pd.Series(dtype='int'),
'Duration': pd.Series(dtype='str'),
'Discount': pd.Series(dtype='float')})
# Using NumPy
dtypes = np.dtype(
[
("Courses", str),
("Fee", int),
("Duration", str),
("Discount", float),
('date',np.datetime64)
]
)
df = pd.DataFrame(np.empty(0, dtype=dtypes))
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# Create Empty DataFrame with column names
df = pd.DataFrame(columns=['species','name','age'])
df.loc[1] = ['dog','Fido','3'] # Populate Row at index 1 (row 1)
df.loc[2] = ['cat','Felix','2'] # Populate Row at index 2 (row 2)
print(df)