import pandas as pd
import numpy as np
# pandas as an array
data = np.array(['p','a','n','d','a', 's'])
myseries = pd.Series(data)
print(myseries)​

import pandas as pd
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)

print(df)

Output :

   col1  col2

0     1     3

1     2     4

import pandas as pd
country_population = {'India': 1600000000, 'China': 1730000000, 'USA': 390000000, 'UK': 450000000}
population = pd.Series(country_population)
#print(population)

country_land = {'India': '2547869 hectares', 'China': '9543578 hectares', 'USA': '5874658 hectares',  'UK': '6354652 hectares'}
area = pd.Series(country_land)
#print(area)

df = pd.DataFrame({'Population': population, 'SpaceOccupied': area})
print(df)

import pandas as pd
index_A = pd.Index([1, 3, 5, 7, 9])
index_B = pd.Index([2, 3, 5, 7, 11])

import pandas as pd
import numpy as np

N=20

df = pd.DataFrame({
   'A': pd.date_range(start='2021-01-18',periods=N,freq='D'),
   'x': np.linspace(0,stop=N-1,num=N),
   'y': np.random.rand(N),
   'C': np.random.choice(['Low','Medium','High'],N).tolist(),
   'D': np.random.normal(100, 10, size=(N)).tolist()
})

#reindex the DataFrame
df_reindexed = df.reindex(index=[0,2,5], columns=['A', 'C', 'B'])

print (df_reindexed)​

Output :

           A              C              B

0 2021-01-18     Medium     NaN

2 2021-01-20     High          NaN

5 2021-01-23     Low           NaN

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1))

Output :

            col1             col2               col3

0    -0.641715     1.031070     -0.208415

1    -1.560385    -0.584403      0.291666

2          NaN           NaN               NaN

3          NaN           NaN               NaN

Reindexing with using methods(bfill or ffill)

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1, method='ffill'))

Output :

                  col1               col2               col3

0        1.332612      -0.479218       -1.016999
1       -1.091319     -0.844934       -0.492755
2       -1.091319     -0.844934       -0.492755
3       -1.091319     -0.844934       -0.492755

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1, method='bfill'))

Output :

                     col1               col2              col3

0           0.526663      -0.450748        0.791112
1          -1.805287       0.641050        1.864871
2                 NaN                 NaN                NaN
3                 NaN                 NaN                NaN

import pandas as pd
info=pd.DataFrame ()
print(info)​

Output :
Empty DataFrame
Columns: []
Index: []

missing_data_count=df.isnull().sum()


We can handle missing values by either replacing the values in the column with 0 as follows:

df[‘column_name’].fillna(0)

Or by replacing it with the mean value of the column

df[‘column_name’] = df[‘column_name’].fillna((df[‘column_name’].mean()))