How to add a column which has three different values at varying lengths?

up vote
-3
down vote

favorite

Hi I have three lists "A","B","C" which contains 10 ,20 30 values respectively.I need to create a dataframe as shown below

Values                   Type

------                  ------ 

ListA_values              A

     .                    A

     .                    A

ListB_Values              B

     .                    B

     . 

ListC_Values              C

     .                    C

     .                    C

     .                    C

The columns here are Values and Type. Sorry Couldnt draw a better figure.

.

asked Nov 20 at 12:31

RAM SHANKER G

146

add a comment |

up vote
-3
down vote

favorite

Hi I have three lists "A","B","C" which contains 10 ,20 30 values respectively.I need to create a dataframe as shown below

Values                   Type

------                  ------ 

ListA_values              A

     .                    A

     .                    A

ListB_Values              B

     .                    B

     . 

ListC_Values              C

     .                    C

     .                    C

     .                    C

The columns here are Values and Type. Sorry Couldnt draw a better figure.

.

asked Nov 20 at 12:31

RAM SHANKER G

146

add a comment |

up vote
-3
down vote

favorite

Hi I have three lists "A","B","C" which contains 10 ,20 30 values respectively.I need to create a dataframe as shown below

Values                   Type

------                  ------ 

ListA_values              A

     .                    A

     .                    A

ListB_Values              B

     .                    B

     . 

ListC_Values              C

     .                    C

     .                    C

     .                    C

The columns here are Values and Type. Sorry Couldnt draw a better figure.

.

asked Nov 20 at 12:31

RAM SHANKER G

146

Hi I have three lists "A","B","C" which contains 10 ,20 30 values respectively.I need to create a dataframe as shown below

Values                   Type

------                  ------ 

ListA_values              A

     .                    A

     .                    A

ListB_Values              B

     .                    B

     . 

ListC_Values              C

     .                    C

     .                    C

     .                    C

The columns here are Values and Type. Sorry Couldnt draw a better figure.

.

python pandas dataframe

asked Nov 20 at 12:31

RAM SHANKER G

146

asked Nov 20 at 12:31

RAM SHANKER G

146

asked Nov 20 at 12:31

RAM SHANKER G

146

asked Nov 20 at 12:31

RAM SHANKER G

146

asked Nov 20 at 12:31

RAM SHANKER G

146

add a comment |

2 Answers
2

active

oldest

votes

up vote
0
down vote

accepted

df1 = pd.DataFrame({'Type': ['A']*100})

df2 = pd.DataFrame({'Type': ['B']*75})

df3 = pd.DataFrame({'Type': ['C']*80})



#Concatenate the DataFrames

df=df1.append(df2).append(df3).reset_index(drop=True)



df.loc[df['Type']=='A','Values']='ListA_values'

df.loc[df['Type']=='B','Values']='ListB_values'

df.loc[df['Type']=='C','Values']='ListC_values'



df=df[['Values','Type']]

A more efficient way will be -

df1 = pd.DataFrame({'Type': ['A']*100})

df1.index = ['ListA_values'] * len(df1)

df2 = pd.DataFrame({'Type': ['B']*75})

df2.index = ['ListB_values'] * len(df2)

df3 = pd.DataFrame({'Type': ['C']*80})

df3.index = ['ListC_values'] * len(df3)



#Concatenate the DataFrames

df=df1.append(df2).append(df3)

df['Values'] = df.index

df=df.reset_index(drop=True)

df=df[['Values','Type']]



df

Out[31]: 

         Values Type

0  ListA_values    A

1  ListA_values    A

2  ListA_values    A

3  ListA_values    A

4  ListA_values    A

5  ListA_values    A

6  ListA_values    A

..            ...  ...

252  ListC_values    C

253  ListC_values    C

254  ListC_values    C

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

add a comment |

up vote
0
down vote

If you have lists, I'd create a dictionary and then use pd.concat + the default DataFrame constructor to make your DataFrame

import pandas as pd



# Whatever your values are, whatever the size.

A = ['ListA_values']*40 

B = ['ListA_values']*80

C = ['ListA_values']*90



d = {'A': A, 'B': B, 'C': C}



pd.concat([pd.DataFrame(v, columns=['Values']).assign(Type=k) for k,v in d.items()], ignore_index=True)

Output:

           Values Type

0    ListA_values    A

1    ListA_values    A

2    ListA_values    A

3    ListA_values    A

4    ListA_values    A

5    ListA_values    A

..            ...  ...

206  ListA_values    C

207  ListA_values    C

208  ListA_values    C

209  ListA_values    C

answered Nov 20 at 15:20

ALollz

10.9k31334

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

up vote
0
down vote

accepted

df1 = pd.DataFrame({'Type': ['A']*100})

df2 = pd.DataFrame({'Type': ['B']*75})

df3 = pd.DataFrame({'Type': ['C']*80})



#Concatenate the DataFrames

df=df1.append(df2).append(df3).reset_index(drop=True)



df.loc[df['Type']=='A','Values']='ListA_values'

df.loc[df['Type']=='B','Values']='ListB_values'

df.loc[df['Type']=='C','Values']='ListC_values'



df=df[['Values','Type']]

A more efficient way will be -

df1 = pd.DataFrame({'Type': ['A']*100})

df1.index = ['ListA_values'] * len(df1)

df2 = pd.DataFrame({'Type': ['B']*75})

df2.index = ['ListB_values'] * len(df2)

df3 = pd.DataFrame({'Type': ['C']*80})

df3.index = ['ListC_values'] * len(df3)



#Concatenate the DataFrames

df=df1.append(df2).append(df3)

df['Values'] = df.index

df=df.reset_index(drop=True)

df=df[['Values','Type']]



df

Out[31]: 

         Values Type

0  ListA_values    A

1  ListA_values    A

2  ListA_values    A

3  ListA_values    A

4  ListA_values    A

5  ListA_values    A

6  ListA_values    A

..            ...  ...

252  ListC_values    C

253  ListC_values    C

254  ListC_values    C

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

add a comment |

up vote
0
down vote

accepted

df1 = pd.DataFrame({'Type': ['A']*100})

df2 = pd.DataFrame({'Type': ['B']*75})

df3 = pd.DataFrame({'Type': ['C']*80})



#Concatenate the DataFrames

df=df1.append(df2).append(df3).reset_index(drop=True)



df.loc[df['Type']=='A','Values']='ListA_values'

df.loc[df['Type']=='B','Values']='ListB_values'

df.loc[df['Type']=='C','Values']='ListC_values'



df=df[['Values','Type']]

A more efficient way will be -

df1 = pd.DataFrame({'Type': ['A']*100})

df1.index = ['ListA_values'] * len(df1)

df2 = pd.DataFrame({'Type': ['B']*75})

df2.index = ['ListB_values'] * len(df2)

df3 = pd.DataFrame({'Type': ['C']*80})

df3.index = ['ListC_values'] * len(df3)



#Concatenate the DataFrames

df=df1.append(df2).append(df3)

df['Values'] = df.index

df=df.reset_index(drop=True)

df=df[['Values','Type']]



df

Out[31]: 

         Values Type

0  ListA_values    A

1  ListA_values    A

2  ListA_values    A

3  ListA_values    A

4  ListA_values    A

5  ListA_values    A

6  ListA_values    A

..            ...  ...

252  ListC_values    C

253  ListC_values    C

254  ListC_values    C

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

add a comment |

up vote
0
down vote

accepted

df1 = pd.DataFrame({'Type': ['A']*100})

df2 = pd.DataFrame({'Type': ['B']*75})

df3 = pd.DataFrame({'Type': ['C']*80})



#Concatenate the DataFrames

df=df1.append(df2).append(df3).reset_index(drop=True)



df.loc[df['Type']=='A','Values']='ListA_values'

df.loc[df['Type']=='B','Values']='ListB_values'

df.loc[df['Type']=='C','Values']='ListC_values'



df=df[['Values','Type']]

A more efficient way will be -

df1 = pd.DataFrame({'Type': ['A']*100})

df1.index = ['ListA_values'] * len(df1)

df2 = pd.DataFrame({'Type': ['B']*75})

df2.index = ['ListB_values'] * len(df2)

df3 = pd.DataFrame({'Type': ['C']*80})

df3.index = ['ListC_values'] * len(df3)



#Concatenate the DataFrames

df=df1.append(df2).append(df3)

df['Values'] = df.index

df=df.reset_index(drop=True)

df=df[['Values','Type']]



df

Out[31]: 

         Values Type

0  ListA_values    A

1  ListA_values    A

2  ListA_values    A

3  ListA_values    A

4  ListA_values    A

5  ListA_values    A

6  ListA_values    A

..            ...  ...

252  ListC_values    C

253  ListC_values    C

254  ListC_values    C

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

df1 = pd.DataFrame({'Type': ['A']*100})

df2 = pd.DataFrame({'Type': ['B']*75})

df3 = pd.DataFrame({'Type': ['C']*80})



#Concatenate the DataFrames

df=df1.append(df2).append(df3).reset_index(drop=True)



df.loc[df['Type']=='A','Values']='ListA_values'

df.loc[df['Type']=='B','Values']='ListB_values'

df.loc[df['Type']=='C','Values']='ListC_values'



df=df[['Values','Type']]

A more efficient way will be -

df1 = pd.DataFrame({'Type': ['A']*100})

df1.index = ['ListA_values'] * len(df1)

df2 = pd.DataFrame({'Type': ['B']*75})

df2.index = ['ListB_values'] * len(df2)

df3 = pd.DataFrame({'Type': ['C']*80})

df3.index = ['ListC_values'] * len(df3)



#Concatenate the DataFrames

df=df1.append(df2).append(df3)

df['Values'] = df.index

df=df.reset_index(drop=True)

df=df[['Values','Type']]



df

Out[31]: 

         Values Type

0  ListA_values    A

1  ListA_values    A

2  ListA_values    A

3  ListA_values    A

4  ListA_values    A

5  ListA_values    A

6  ListA_values    A

..            ...  ...

252  ListC_values    C

253  ListC_values    C

254  ListC_values    C

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

edited Nov 20 at 15:06

answered Nov 20 at 12:51

cph_sto

667216

answered Nov 20 at 12:51

cph_sto

667216

answered Nov 20 at 12:51

cph_sto

667216

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

add a comment |

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

What if i have 100 'A' values 75 'B' Values and 80 'C' values?
– RAM SHANKER G
Nov 20 at 13:06

I have made the changes. You can copy the code exactly.
– cph_sto
Nov 20 at 13:18

Would you mind accepting the answer if it helped you?
– cph_sto
Nov 22 at 20:21

Lol sure! :) Thanks a lot!
– RAM SHANKER G
Nov 23 at 13:30

add a comment |

up vote
0
down vote

If you have lists, I'd create a dictionary and then use pd.concat + the default DataFrame constructor to make your DataFrame

import pandas as pd



# Whatever your values are, whatever the size.

A = ['ListA_values']*40 

B = ['ListA_values']*80

C = ['ListA_values']*90



d = {'A': A, 'B': B, 'C': C}



pd.concat([pd.DataFrame(v, columns=['Values']).assign(Type=k) for k,v in d.items()], ignore_index=True)

Output:

           Values Type

0    ListA_values    A

1    ListA_values    A

2    ListA_values    A

3    ListA_values    A

4    ListA_values    A

5    ListA_values    A

..            ...  ...

206  ListA_values    C

207  ListA_values    C

208  ListA_values    C

209  ListA_values    C

answered Nov 20 at 15:20

ALollz

10.9k31334

add a comment |

up vote
0
down vote

If you have lists, I'd create a dictionary and then use pd.concat + the default DataFrame constructor to make your DataFrame

import pandas as pd



# Whatever your values are, whatever the size.

A = ['ListA_values']*40 

B = ['ListA_values']*80

C = ['ListA_values']*90



d = {'A': A, 'B': B, 'C': C}



pd.concat([pd.DataFrame(v, columns=['Values']).assign(Type=k) for k,v in d.items()], ignore_index=True)

Output:

           Values Type

0    ListA_values    A

1    ListA_values    A

2    ListA_values    A

3    ListA_values    A

4    ListA_values    A

5    ListA_values    A

..            ...  ...

206  ListA_values    C

207  ListA_values    C

208  ListA_values    C

209  ListA_values    C

answered Nov 20 at 15:20

ALollz

10.9k31334

add a comment |

up vote
0
down vote

If you have lists, I'd create a dictionary and then use pd.concat + the default DataFrame constructor to make your DataFrame

import pandas as pd



# Whatever your values are, whatever the size.

A = ['ListA_values']*40 

B = ['ListA_values']*80

C = ['ListA_values']*90



d = {'A': A, 'B': B, 'C': C}



pd.concat([pd.DataFrame(v, columns=['Values']).assign(Type=k) for k,v in d.items()], ignore_index=True)

Output:

           Values Type

0    ListA_values    A

1    ListA_values    A

2    ListA_values    A

3    ListA_values    A

4    ListA_values    A

5    ListA_values    A

..            ...  ...

206  ListA_values    C

207  ListA_values    C

208  ListA_values    C

209  ListA_values    C

answered Nov 20 at 15:20

ALollz

10.9k31334

If you have lists, I'd create a dictionary and then use pd.concat + the default DataFrame constructor to make your DataFrame

import pandas as pd



# Whatever your values are, whatever the size.

A = ['ListA_values']*40 

B = ['ListA_values']*80

C = ['ListA_values']*90



d = {'A': A, 'B': B, 'C': C}



pd.concat([pd.DataFrame(v, columns=['Values']).assign(Type=k) for k,v in d.items()], ignore_index=True)

Output:

           Values Type

0    ListA_values    A

1    ListA_values    A

2    ListA_values    A

3    ListA_values    A

4    ListA_values    A

5    ListA_values    A

..            ...  ...

206  ListA_values    C

207  ListA_values    C

208  ListA_values    C

209  ListA_values    C

answered Nov 20 at 15:20

ALollz

10.9k31334

answered Nov 20 at 15:20

ALollz

10.9k31334

answered Nov 20 at 15:20

ALollz

10.9k31334

answered Nov 20 at 15:20

ALollz

10.9k31334

add a comment |

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