Insert a Row to Pandas Dataframe

Inserting a new row in pandas dataframe

So, given the following toy dataframe:

import pandas as pd

df = pd.DataFrame(
    {
        "Prec": {
            "2010-01-01": 0.585135,
            "2012-12-30": 0.100535,
        },
        "Tmax": {
            "2010-01-01": 3.901162,
            "2012-12-30": -3.498832,
        },
        "Tmin": {
            "2010-01-01": -2.057929,
            "2012-12-30": -8.125136,
        },
        "Tmean": {
            "2010-01-01": 0.921617,
            "2012-12-30": -5.811984,
        },
    }
)

You can do it like this:

df.index = pd.to_datetime(df.index)

new_row = df.copy()[df.index == "2012-12-30"]

new_row.index = new_row.index + pd.Timedelta(days=1)

df = pd.concat([df, new_row]).sort_index(ignore_index=True)

print(df)
# Output
                Prec      Tmax      Tmin     Tmean
2010-01-01  0.585135  3.901162 -2.057929  0.921617
2012-12-30  0.100535 -3.498832 -8.125136 -5.811984
2012-12-31  0.100535 -3.498832 -8.125136 -5.811984

Pandas: Conditionally insert rows into DataFrame while iterating through rows in the middle

You are trying to add a row beyond the dataframe's scope (size/capacity, so to say). You can get the the size of dataframe by using dataframe.shape().

If you need to, you would have to extend the index of the dataframe using set_index() when you try to add a row at the end/after the last row. This should solve your issue.

You could also use dataframe.append() to add new rows.

Another possible solution would be to use integer slicing with iloc. iloc doesn't give an error with slicing, but again, going beyond the scope of the dataframe would still be an issue you have to fix before appending anyhting.

pandas: insert a row after a row where the column contains a specific value

Use concat by helper DataFrame filtered by None or misisng values by Series.isna, set values in columns in DataFrame.assign and then sort index by DataFrame.sort_index with created default indices:

df = (pd.concat([df, df[df.label.isna()].assign(text='new_val',label='new_val')])
        .sort_index()
        .reset_index(drop=True))
print (df)
      text    label
0     open        O
1      the        B
2     door        D
3      val     None
4  new_val  new_val
5    close        C
6      the        E
7     door        N
8      val     None
9  new_val  new_val

Insert empty row after every Nth row in pandas dataframe

The following should scale well with the size of the DataFrame since it doesn't iterate over the rows and doesn't create intermediate DataFrames.

import pandas as pd

df = pd.DataFrame(columns=['a','b'],data=[[3,4],
    [5,5],[9,3],[1,2],[9,9],[6,5],[6,5],[6,5],[6,5],
    [6,5],[6,5],[6,5],[6,5],[6,5],[6,5],[6,5],[6,5]])

def add_empty_rows(df, n_empty, period):
    """ adds 'n_empty' empty rows every 'period' rows  to 'df'. 
        Returns a new DataFrame. """
    
    # to make sure that the DataFrame index is a RangeIndex(start=0, stop=len(df)) 
    # and that the original df object is not mutated. 
    df = df.reset_index(drop=True)
    
    # length of the new DataFrame containing the NaN rows
    len_new_index = len(df) + n_empty*(len(df) // period)
    # index of the new DataFrame
    new_index = pd.RangeIndex(len_new_index)
    
    # add an offset (= number of NaN rows up to that row) 
    # to the current df.index to align with new_index. 
    df.index += n_empty * (df.index
                             .to_series()
                             .groupby(df.index // period)
                             .ngroup())
    
    # reindex by aligning df.index with new_index. 
    # Values of new_index not present in df.index are filled with NaN.
    new_df = df.reindex(new_index)
    
    return new_df

Tests:

# original df
>>> df

    a  b
0   3  4
1   5  5
2   9  3
3   1  2
4   9  9
5   6  5
6   6  5
7   6  5
8   6  5
9   6  5
10  6  5
11  6  5
12  6  5
13  6  5
14  6  5
15  6  5
16  6  5

# add 2 empty rows every 3 rows
>>> add_empty_rows(df, 2, 3)

      a    b
0   3.0  4.0
1   5.0  5.0
2   9.0  3.0
3   NaN  NaN
4   NaN  NaN
5   1.0  2.0
6   9.0  9.0
7   6.0  5.0
8   NaN  NaN
9   NaN  NaN
10  6.0  5.0
11  6.0  5.0
12  6.0  5.0
13  NaN  NaN
14  NaN  NaN
15  6.0  5.0
16  6.0  5.0
17  6.0  5.0
18  NaN  NaN
19  NaN  NaN
20  6.0  5.0
21  6.0  5.0
22  6.0  5.0
23  NaN  NaN
24  NaN  NaN
25  6.0  5.0
26  6.0  5.0

# add 5 empty rows every 4 rows
>>> add_empty_rows(df, 5, 4)

      a    b
0   3.0  4.0
1   5.0  5.0
2   9.0  3.0
3   1.0  2.0
4   NaN  NaN
5   NaN  NaN
6   NaN  NaN
7   NaN  NaN
8   NaN  NaN
9   9.0  9.0
10  6.0  5.0
11  6.0  5.0
12  6.0  5.0
13  NaN  NaN
14  NaN  NaN
15  NaN  NaN
16  NaN  NaN
17  NaN  NaN
18  6.0  5.0
19  6.0  5.0
20  6.0  5.0
21  6.0  5.0
22  NaN  NaN
23  NaN  NaN
24  NaN  NaN
25  NaN  NaN
26  NaN  NaN
27  6.0  5.0
28  6.0  5.0
29  6.0  5.0
30  6.0  5.0
31  NaN  NaN
32  NaN  NaN
33  NaN  NaN
34  NaN  NaN
35  NaN  NaN
36  6.0  5.0

Pandas: Inserting rows based on conditions?

You aren't changing your dataframe in the function at all. You are simply printing some statements. You don't really need a custom function for what you want to do.

Try:

melt the dataframe to create the required structure.
Filter to keep rows where the value is greater than 0.
Re-format the "product" column as required (removing the "_count").

melted = df.melt(["id", "status", "type", "location"],
                 ["bb_count","vo_count","tv_count"],
                 var_name="product")
output = melted[melted["value"].gt(0)].drop("value",axis=1)
output["product"] = output["product"].str.replace("_count","")
                                         .replace({"bb": "broadband",
                                                   "vo":"fixedvoice",
                                                   "tv":"television"})

>>> output
    id  status type  location     product
0  123    open    r  hongkong   broadband
1  456    open    r  hongkong   broadband
4  456    open    r  hongkong  fixedvoice
5  456  closed    p     India  fixedvoice
6  123    open    r  hongkong  television
7  456    open    r  hongkong  television
8  456  closed    p     India  television