title: pandas: looping through several dataframes for efficiency
body: <p>I'm just learning pandas working on a Jupyter Notebook. I have 10 datasets containing daily temperatures from different years. However, the dates are in different columns for months, days and years, and in a different column the temperature. The original data have spaces in the column names. For example, the information for df1info() is the following:</p>
  
  <pre><code>&lt;class 'pandas.core.frame.DataFrame'&gt;
  RangeIndex: 17301 entries, 0 to 17300
  Data columns (total 4 columns):
  agno      17301 non-null int64
  mes      17301 non-null int64
  dia      17301 non-null int64
  valor    17301 non-null float64
  dtypes: float64(1), int64(3)
  memory usage: 540.7 KB
  </code></pre>
  
  <p>I did write a the following code which perform the job after checking different solutions in this community, I wonder about a more efficient and pandorable way to do it.</p>
  
  <pre><code>list_df = [df1,df2,df3,df4,df5,df6,df7,df8,df9,df10]
  for i, df in enumerate(list_dfs):
  df = list_df[i]
  df = df.rename(columns=lambda x: x.strip())
  df['mes'] = df['mes'].astype(str)
  df['dia'] = df['dia'].astype(str)
  df['combined']=df['agno'].astype(str) + "-" + df["mes"] + "-" + df["dia"]
  df["date"]= pd.to_datetime(df["combined"])
  df = df[['date','valor']]
  list_df[i] = df
  i = i + 1
  </code></pre>
  
  <p>I would appreciate your help. Thanks in advance.</p>