Pandas - DataFrame
A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns.
pandas.DataFrame
pandas.DataFrame( data, index, columns, dtype, copy)
Create DataFrame
A pandas DataFrame can be created using various inputs like: * Lists * dict * Series * Numpy ndarrays * Another DataFrame
Create an Empty DataFrame
A basic DataFrame, which can be created is an Empty Dataframe.
Example
import pandas as pd
df = pd.DataFrame()
print(df)
>>> Empty DataFrame
Columns: []
Index: []
Create a DataFrame from Lists
The DataFrame can be created using a single list or a list of lists.
Example
import pandas as pd
data = [1,2,3,4,5]
df = pd.DataFrame(data)
print(df)
>>> 0
0 1
1 2
2 3
3 4
4 5
Example
import pandas as pd
data = [['Bhavya',10],['Manasa',12],['Nandhini',13],['Keerthi',14]]
df = pd.DataFrame(data,columns=['Name','level'])
print(df)
>>> Name level
0 Bhavya 10
1 Manasa 12
2 Nandhini 13
3 Keerthi 14
Example
import pandas as pd
data = [['Bhavya',10],['Manasa',12],['Nandhini',13],['Keerthi',14]]
df = pd.DataFrame(data,columns=['Name','level'],dtype=float)
print(df)
>>> Name level
0 Bhavya 10.0
1 Manasa 12.0
2 Nandhini 13.0
3 Keerthi 14.0
The dtype parameter changes the type of level column to floating point.
Create a DataFrame from Dict of ndarrays / Lists
All the ndarrays must be of same length. If index is passed, then the length of the index should equal to the length of the arrays.
If no index is passed, then by default, index will be range(n), where n is the array length.
Example
import pandas as pd
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data)
print(df)
>>> Age Name
0 28 Tom
1 34 Jack
2 29 Steve
3 42 Ricky
Observe the values 0,1,2,3. They are the default index assigned to each using the function range(n).
Example
import pandas as pd
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
print(df)
>>> Age Name
rank1 28 Tom
rank2 34 Jack
rank3 29 Steve
rank4 42 Ricky
Create a DataFrame from List of Dicts
List of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names.
Example
import pandas as pd
data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
df = pd.DataFrame(data)
print(df)
>>> a b c
0 1 2 NaN
1 5 10 20.0
Example
import pandas as pd
data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
#With two column indices, values same as dictionary keys
df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])
#With two column indices with one index with other name
df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
print(df1)
>>> a b
first 1 2
second 5 10
print(df2)
>>> a b1
first 1 NaN
second 5 NaN
Observe, df2 DataFrame is created with a column index other than the dictionary key; thus, appended the NaN’s in place. Whereas, df1 is created with column indices same as dictionary keys, so NaN’s appended.
Create a DataFrame from Dict of Series
Dictionary of Series can be passed to form a DataFrame. The resultant index is the union of all the series indexes passed.
Example
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print(df)
>>> one two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4
Accesssing the elements from the DataFrame
columns
Column selection
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print(df['one'])
>>> a 1.0
b 2.0
c 3.0
d NaN
Name: one, dtype: float64
Column Addition
mport pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print ("Adding a new column by passing as Series:")
df['three']=pd.Series([10,20,30],index=['a','b','c'])
print(df)
>>> one two three
a 1.0 1 10.0
b 2.0 2 20.0
c 3.0 3 30.0
d NaN 4 NaN
Column Deletion
# Using the previous DataFrame, we will delete a column
# using del function
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']),
'three' : pd.Series([10,20,30], index=['a','b','c'])}
df = pd.DataFrame(d)
print ("Our dataframe is:")
print df
>>> Our dataframe is:
one three two
a 1.0 10.0 1
b 2.0 20.0 2
c 3.0 30.0 3
d NaN NaN 4
# using del function
print ("Deleting the first column using DEL function:")
del df['one']
print df
>>> Deleting the first column using DEL function:
three two
a 10.0 1
b 20.0 2
c 30.0 3
d NaN 4
# using pop function
print ("Deleting another column using POP function:")
df.pop('two')
print df
>>> Deleting another column using POP function:
three
a 10.0
b 20.0
c 30.0
d NaN
rows
Row selection
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print df.loc['b']
>>> one 2.0
two 2.0
Name: b, dtype: float64
# select by integer location
print df.iloc[2]
>>> one 3.0
two 3.0
Name: c, dtype: float64
# Slice Rows
print df[2:4]
>>> one two
c 3.0 3
d NaN 4
Row Addition
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])
df = df.append(df2)
print df
>>> a b
0 1 2
1 3 4
0 5 6
1 7 8
Row Deletion
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])
df = df.append(df2)
# Drop rows with label 0
df = df.drop(0)
print df
>>> a b
1 3 4
1 7 8