For loop (nested) for value_counts() with Pandas

I have a DataFrame like the following one:

enter image description here

Now I would like to do value_counts(), but the problem I have is when I use the following code:

with open('out.csv', 'w') as out:

    for col in df.columns:

        res = df[col].value_counts()

                     .reset_index()

                     .rename(columns={col: 'count', 'index': col})



        res.to_csv(out, index=False)

I get the following wrong results:

enter image description here

But what I need is the following:

enter image description here

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

add a comment |

I have a DataFrame like the following one:

enter image description here

Now I would like to do value_counts(), but the problem I have is when I use the following code:

with open('out.csv', 'w') as out:

    for col in df.columns:

        res = df[col].value_counts()

                     .reset_index()

                     .rename(columns={col: 'count', 'index': col})



        res.to_csv(out, index=False)

I get the following wrong results:

enter image description here

But what I need is the following:

enter image description here

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

add a comment |

I have a DataFrame like the following one:

enter image description here

Now I would like to do value_counts(), but the problem I have is when I use the following code:

with open('out.csv', 'w') as out:

    for col in df.columns:

        res = df[col].value_counts()

                     .reset_index()

                     .rename(columns={col: 'count', 'index': col})



        res.to_csv(out, index=False)

I get the following wrong results:

enter image description here

But what I need is the following:

enter image description here

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

I have a DataFrame like the following one:

enter image description here

Now I would like to do value_counts(), but the problem I have is when I use the following code:

with open('out.csv', 'w') as out:

    for col in df.columns:

        res = df[col].value_counts()

                     .reset_index()

                     .rename(columns={col: 'count', 'index': col})



        res.to_csv(out, index=False)

I get the following wrong results:

enter image description here

But what I need is the following:

enter image description here

python python-3.x pandas dataframe for-loop

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

edited Nov 22 '18 at 9:49

jpp

101k2163112

edited Nov 22 '18 at 9:49

jpp

101k2163112

edited Nov 22 '18 at 9:49

jpp

101k2163112

asked Nov 22 '18 at 9:26

pranesh santhanam

asked Nov 22 '18 at 9:26

pranesh santhanam

asked Nov 22 '18 at 9:26

pranesh santhanam

add a comment |

1 Answer
1

active

oldest

votes

`value_counts` + `melt`

You can apply value_counts to each series, then melt your dataframe:

df = pd.DataFrame({'01-01-2018': [1, 2, 3, 4, 1, 2],

                   '02-01-2018': [4, 2, 3, 1, 3, 1]})



res = df.apply(pd.Series.value_counts).reset_index()

res = pd.melt(res, id_vars='index')

res.columns = ['Code', 'Date', 'Freq']



print(res)



   Code        Date  Freq

0     1  01-01-2018     2

1     2  01-01-2018     2

2     3  01-01-2018     1

3     4  01-01-2018     1

4     1  02-01-2018     2

5     2  02-01-2018     1

6     3  02-01-2018     2

7     4  02-01-2018     1

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

`value_counts` + `melt`

You can apply value_counts to each series, then melt your dataframe:

df = pd.DataFrame({'01-01-2018': [1, 2, 3, 4, 1, 2],

                   '02-01-2018': [4, 2, 3, 1, 3, 1]})



res = df.apply(pd.Series.value_counts).reset_index()

res = pd.melt(res, id_vars='index')

res.columns = ['Code', 'Date', 'Freq']



print(res)



   Code        Date  Freq

0     1  01-01-2018     2

1     2  01-01-2018     2

2     3  01-01-2018     1

3     4  01-01-2018     1

4     1  02-01-2018     2

5     2  02-01-2018     1

6     3  02-01-2018     2

7     4  02-01-2018     1

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

add a comment |

`value_counts` + `melt`

You can apply value_counts to each series, then melt your dataframe:

df = pd.DataFrame({'01-01-2018': [1, 2, 3, 4, 1, 2],

                   '02-01-2018': [4, 2, 3, 1, 3, 1]})



res = df.apply(pd.Series.value_counts).reset_index()

res = pd.melt(res, id_vars='index')

res.columns = ['Code', 'Date', 'Freq']



print(res)



   Code        Date  Freq

0     1  01-01-2018     2

1     2  01-01-2018     2

2     3  01-01-2018     1

3     4  01-01-2018     1

4     1  02-01-2018     2

5     2  02-01-2018     1

6     3  02-01-2018     2

7     4  02-01-2018     1

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

add a comment |

`value_counts` + `melt`

You can apply value_counts to each series, then melt your dataframe:

df = pd.DataFrame({'01-01-2018': [1, 2, 3, 4, 1, 2],

                   '02-01-2018': [4, 2, 3, 1, 3, 1]})



res = df.apply(pd.Series.value_counts).reset_index()

res = pd.melt(res, id_vars='index')

res.columns = ['Code', 'Date', 'Freq']



print(res)



   Code        Date  Freq

0     1  01-01-2018     2

1     2  01-01-2018     2

2     3  01-01-2018     1

3     4  01-01-2018     1

4     1  02-01-2018     2

5     2  02-01-2018     1

6     3  02-01-2018     2

7     4  02-01-2018     1

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

`value_counts` + `melt`

You can apply value_counts to each series, then melt your dataframe:

df = pd.DataFrame({'01-01-2018': [1, 2, 3, 4, 1, 2],

                   '02-01-2018': [4, 2, 3, 1, 3, 1]})



res = df.apply(pd.Series.value_counts).reset_index()

res = pd.melt(res, id_vars='index')

res.columns = ['Code', 'Date', 'Freq']



print(res)



   Code        Date  Freq

0     1  01-01-2018     2

1     2  01-01-2018     2

2     3  01-01-2018     1

3     4  01-01-2018     1

4     1  02-01-2018     2

5     2  02-01-2018     1

6     3  02-01-2018     2

7     4  02-01-2018     1

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

edited Nov 22 '18 at 9:53

answered Nov 22 '18 at 9:37

jpp

101k2163112

answered Nov 22 '18 at 9:37

jpp

101k2163112

answered Nov 22 '18 at 9:37

jpp

101k2163112

add a comment |

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