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Python Pandas Problems
In this python pandas program, we will import an xlsx file using the pandas library.
import pandas as pd
df = pd.read_excel("file name")
print(df)
import a CSV file
In this python pandas program, we will import a CSV file using the pandas library.
import pandas as pd
df = pd.read_csv("file name")
print(df)
In this python pandas program, we will replace NaNs with the median value using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex','John','Peter','Klaus'],
'Age':[30,np.nan,29,22]})
print("Original Dataframe: \n",df)
df['Age'].fillna(df['Age'].median(),inplace=True)
print("After replacing missing values by mean: \n",df)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus 22.0
After replacing missing values by mean:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John 29.0
2 3 Peter 29.0
3 4 Klaus 22.0
In this python pandas program, we will replace the NaN values with mode value in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4,5],
'Name':['Alex',np.nan,'Peter','Klaus','Stefan'],
'Age':[30,np.nan,29,22,22]})
print("Original Dataframe: \n",df)
result = df.fillna(df.mode().iloc[0])
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 NaN NaN
2 3 Peter 29.0
3 4 Klaus 22.0
4 5 Stefan 22.0
Sr.no. Name Age
0 1 Alex 30.0
1 2 Alex 22.0
2 3 Peter 29.0
3 4 Klaus 22.0
4 5 Stefan 22.0
In this python pandas program, we will replace NaNs with mean in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex','John','Peter','Klaus'],
'Age':[30,np.nan,29,22]})
print("Original Dataframe: \n",df)
df['Age'].fillna(df['Age'].mean(),inplace=True)
print("After replacing missing values by mean: \n",df)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John NaN
2 3 Peter 29.0
3 4 Klaus 22.0
After replacing missing values by mean:
Sr.no. Name Age
0 1 Alex 30.0
1 2 John 27.0
2 3 Peter 29.0
3 4 Klaus 22.0
In this python pandas program, we will replace NaNs with the value from the next row in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex',np.nan,'Peter','Klaus'],
'Age':[30,np.nan,29,22]})
print("Original Dataframe: \n",df)
print("Fill the rows where all elements are missing with next values:")
result = df.fillna(method='bfill')
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 NaN NaN
2 3 Peter 29.0
3 4 Klaus 22.0
Fill the rows where all elements are missing with next values:
Sr.no. Name Age
0 1 Alex 30.0
1 2 Peter 29.0
2 3 Peter 29.0
3 4 Klaus 22.0
In this python pandas program, we will replace NaNs with the value from the previous row in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex',np.nan,'Peter','Klaus'],
'Age':[30,np.nan,29,np.nan]})
print("Original Dataframe: \n",df)
print("Fill the rows where all elements are missing with previous values:")
result = df.fillna(method='pad')
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 NaN NaN
2 3 Peter 29.0
3 4 Klaus NaN
Fill the rows where all elements are missing with previous values:
Sr.no. Name Age
0 1 Alex 30.0
1 2 Alex 30.0
2 3 Peter 29.0
3 4 Klaus 29.0
In this python pandas program, we will drop the rows where all elements are missing in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,np.nan,3,4],
'Name':['Alex',np.nan,'Peter','Klaus'],
'Age':[30,np.nan,29,np.nan]})
print("Original Dataframe: \n",df)
print("Drop the rows where all elements are missing:")
result = df.dropna(how='all')
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1.0 Alex 30.0
1 NaN NaN NaN
2 3.0 Peter 29.0
3 4.0 Klaus NaN
Drop the rows where all elements are missing:
Sr.no. Name Age
0 1.0 Alex 30.0
2 3.0 Peter 29.0
3 4.0 Klaus NaN
In this python pandas program, we will drop the columns where at least one element is missing using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex',np.nan,'Peter','Klaus'],
'Age':[30,np.nan,29,np.nan]})
print("Original Dataframe: \n",df)
result = df.dropna(axis=1)
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 NaN NaN
2 3 Peter 29.0
3 4 Klaus NaN
Sr.no.
0 1
1 2
2 3
3 4
In this python pandas program, we will Drop the rows where at least one element is missing in a DataFrame using the pandas library.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Sr.no.':[1,2,3,4],
'Name':['Alex',np.nan,'Peter','Klaus'],
'Age':[30,np.nan,29,np.nan]})
print("Original Dataframe: \n",df)
result = df.dropna()
print(result)
Output :
Original Dataframe:
Sr.no. Name Age
0 1 Alex 30.0
1 2 NaN NaN
2 3 Peter 29.0
3 4 Klaus NaN
Sr.no. Name Age
0 1 Alex 30.0
2 3 Peter 29.0