Python populating dataframe in pandas from text files

In [44]:

cols = ['a', 'b', 'b', 'a', 'c']
df[cols]
Out[44]:
   a  b  b  a  c
0  1  4  4  1  6
1  3  2  2  3  4
2  4  1  1  4  5

[3 rows x 5 columns]

    batcol = pd.DataFrame(df1.columns)
    batcol = batcol.iloc[6:-1]
    batcol = batcol.reset_index(drop=True)
    for index, row in batcol.iterrows():
        for i, rows in df2.iterrows():
            df2.loc[i, row] = df1[((df1['date'] < rows['date']) & (df1['team'] == rows['opp'])) == True].mean()[row].sum()

df = pd.DataFrame(index=[1, 2, 3])
df['First'] = 68
df['Second'] = 157

print(df)

   First  Second
1     68     157
2     68     157
3     68     157

newdf.combine_first(pd.pivot_table(df,index='Date',columns='ID',values='Value',aggfunc='sum'))
Out[442]: 
          a     b
1/1/17  2.0   5.0
1/2/17  3.0  13.0
1/3/17  4.0   NaN

import pandas as pd

# Initialize example dataframe
data = [
    ["You can extract_insights on product reception", "insights"],
    ["user various sources like extract_insights etc.", "insights"],
    ["some other sourced mail by using signals from state art", "text"],
]
df = pd.DataFrame(data=data, columns=["ColA", "ColB"])

# Create column C with comparison results
df["ColC"] = [
    "AVB" if (b in a) else "NAVB"
    for (a, b) in zip(df["ColA"], df["ColB"])
]

print(df)
# Output:
#                                                 ColA      ColB  ColC
# 0      You can extract_insights on product reception  insights   AVB
# 1    user various sources like extract_insights etc.  insights   AVB
# 2  some other sourced mail by using signals from ...      text  NAVB